design of an image-based social network system

SIM UNIVERSITY SCHOOL OF SCIENCE AND TECHNOLOGY DESIGN OF AN IMAGE-BASED SOCIAL NETWORK SYSTEM STUDENT : LIU YUE (PI NO. W0704240) SUPERVISOR : TIAN JING PROJECT CODE : JAN2011/ICT/046 A project report submitted to SIM University in partial fulfilment of the requirements for the degree of Bachelor of Information Technology and Business November 2011 TABLE OF CONTENTS Page ABSTRACT ---------------------------------------------------------------------------------------------------------------- 3 ACKNOWLEDGEMENT ------------------------------------------------------------------------------------------------ 3 CHAPTER ONE: INTRODUCTION AND PROBLEM STATEMENT 1.1 INTRODUCTION --------------------------------------------------------------------------------------------- 4 1.1.1 HISTORY ----------------------------------------------------------------------------------------------- 4 1.1.2 SOCIAL IMPACTS ----------------------------------------------------------------------------------- 4 1.1.3 TYPICAL STRUCTURE 1.1.3.1 BASICS --------------------------------------------------------------------------------------- 5 1.1.3.2 ADDITIONAL FEATURES --------------------------------------------------------------- 6 1.1.3.3 PROPOSED APPROACH AND METHOD TO BE EMPLOYED FOR PROJECT -------------------------------------- 8 1.2 PROBLEM STATEMENT 1.2.1 SOFTWARE APPLICATION AND WEBPAGE INTERFACE ------------------------------- 9 1.2.2 DATABASE DESIGN FOR A DYNAMIC ENVIRONMENT -------------------------------- 9 1.2.3 CHOSEN OF WEBSITE DESIGN LANGUAGE FOR A DYNAMIC ENVIRONMENT ------------------------------------------- 9 CHAPTER TWO: LITERATURE REVIEW AND CITATIONS 2.1 CONTENT-BASED IMAGE RETRIEVAL TECHNOLOGY 2.1.1 INTRODUCTION ---------------------------------------------------------------------------------- 10 2.1.2 RELATEDWORK ---------------------------------------------------------------------------------- 11 2.1.3 PROPOSED CBIR MODEL ---------------------------------------------------------------------- 12 2.1.4 COLOR ---------------------------------------------------------------------------------------------- 12 2.1.5 TEXTURE ------------------------------------------------------------------------------------------- 13 2.1.6 EDGE DENSITY ----------------------------------------------------------------------------------- 13 2.1.7 EXPERIMENTAL SETUP AND RESULTS --------------------------------------------------- 15 2.1.8 RETRIEVAL EFFICIENCY ---------------------------------------------------------------------- 16 2.1.9 CONCLUSION AND FUTURE WORK -------------------------------------------------------- 17 2.2 APACHE TOMCAT 7 ------------------------------------------------------------------------------------- 18 CHAPTER THREE: INTRUDUCTION TO SELECT OF METHODS AND ANALYSIS 3.1 SOFTWARE APPLICATION AND WEBPAGE INTERFACE ------------------------------------- 19 3.2 DATABASE DESIGN FOR A DYNAMIC ENVIRONMENT -------------------------------------- 19 3.3 CHOSEN OF WEBSITE DESIGN LANGUAGE FOR A DYNAMIC ENVIRONMENT ------ 19 CHAPTER FOUR: DETAILED PRESENTATION OF IMAGE-BASED SOCIAL NETOWORK SYSTEM 4.1 IMAGE-BASED SOCIAL NETWORK SYSTEM PROCESS --------------------------------------- 20 4.1.1 SEARCH BY USERS ------------------------------------------------------------------------------ 21 4.1.2 SEARCH BY PHOTO ----------------------------------------------------------------------------- 26 4.2 PROJECT MANAGEMENT 4.2.1 PROJECT MANAGEMENT LIFECYCLE ----------------------------------------------------- 30 4.2.2 WORK BREAKDOWN STRUCTURE (WBS) ------------------------------------------------ 31 4.2.3 PROJECT GANTT CHART ---------------------------------------------------------------------- 32 4.2.4 RISK ASSESSMENT 4.2.4.1 RISK IDENTIFICATION -------------------------------------------------------------- 32 4.2.4.2 MITIGATION STEPS ------------------------------------------------------------------ 33 CHAPTER FIVE: EVALUATION OF EXPERIMENTAL/THEORETICAL RESULTS --------------------- 33 CHAPTER SIX: CONCLUSIONS AND REFLECTIONS 6.1 CONCLUSIONS ------------------------------------------------------------------------------------------- 34 6.2 REFLECTIONS -------------------------------------------------------------------------------------------- 36 REFERENCES ------------------------------------------------------------------------------------------------------------ 37 APPENDIX ---------------------------------------------------------------------------------------------------------------- 39 ICT 499 CAPSTONE PROJECT 2 ABSTRACT Over the recent years, the social network service (e.g. Facebook, Friendster) is a very popular service that focuses on building and reflecting of social relations among people. For example, who share interests and/or activities, will be suggested to be other people’s friend who has the same interests and/or activities. One person uploads a photo with groups of people, the social network system will list down all the names of the people inside the photo based on their face images, and suggest them to be friends in the network system. This project aims to design such social network from a new prospective---image-based. The social relations among users are established based on their image sharing profiles. For example, if a user uploads a photo of soccer activity, then other users having photos related to soccer activities could be suggested to be friend of this user. This new social network system will be a new revolution for the social network services, the current social network services have the same limitation – those information link people together must be exactly the same. The new social network system can break the limitation, users do not need to upload the same photos then can be linked together, this system will analyze the photos and classify them into different categories, it will select the photos with same category and link them together, suggest the users who upload the same category photos to be friends. ACKNOWLEDGEMENT I would like to express my heartfelt gratitude towards my supervisor Tian Jing, for his invaluable advices and guidance for this project during this whole year. He had been very patient and generous towards the sharing of his expertise in Content-based image retrieval knowledge and creative idea for social network system with me. Last but not least, I also would like to thank the senior patch student who gave me the idea for using website as a platform to perform my project, and the suggestion for choosing the entire necessary software I need to use for my project. ICT 499 CAPSTONE PROJECT 3 CHAPTER ONE: INTRODUCTION AND PROBLEM STATEMENT 1.1. INTRODUCTION A social network is a social structure made up of individuals (or organizations), which are tied (connected) by one or more specific types of interdependency, such as friendship, kinship, common interest, financial exchange, dislike, sexual relationships, or relationships of beliefs, knowledge or prestige. [1] A social network service essentially consists of a representation of each user (often a profile), his/her social links, and a variety of additional services. Most social network services are web based and provide means for users to interact over the internet, such as email and instant messaging. The main types of social networking services are those which contain category places (such as former school year or classmates), means to connect with friends (usually with self-description pages) and a recommendation system linked to trust. 1.1.1. HISTORY Early social networking on the World Wide Web began in the form of generalized online communities such as Theglobe.com (1994), Geocities (1995) and Tripod.com (1995). Many of these early communities focused on bringing people together to interact with each other through chat rooms, and encouraged users to share personal information and ideas via personal WebPages by providing easy-to-use publishing tools and free or inexpensive web space. New social networking methods were developed by the end of the 1990s, and many sites began to develop more advanced features for users to find and manage friends. This newer generation of social networking sites began to flourish with the emergence of Friendster in 2002, and soon became part of the Internet mainstream. After two years, Facebook was found by Mark Zuckerberg, and it has since become the largest social networking site in the world. Today, it is estimated that there are now over 200 active sites using a wide variety of social networking models. 1.1.2. SOCIAL IMPACTS Web based social network services make it possible to connect people who share interests and activities across political, economic, and geographic borders. Through e-mail and instant messaging, online communities are created where a gift economy and reciprocal altruism are encouraged through cooperation. ICT 499 CAPSTONE PROJECT 4 There are some application domains for social network services: Government applications: social networking tools serve as a quick and easy way for the government to get the opinion of the public and to keep the public updated on their activity. Business applications: the use of social network services in an enterprise context presents the potential of having a major impact on the world of business and work. Dating applications: many social networks provide an online environment for people to communicate and exchange personal information for dating purposes. Educational applications: social networks focused on supporting relationships between teachers and their students are now used for learning, educator professional development, and content sharing. Medical applications: social networks are beginning to be adopted by healthcare professionals as a means to manage institutional knowledge, disseminate peer to peer knowledge and to highlight individual physicians and institutions. 1.1.3. TYPICAL STRUCTURE 1.1.3.1. BASICS Social networking sites tend to share some conventional features. Most often, individual users are encouraged to create profiles containing various information about themselves. Users can often upload pictures of themselves to their profiles, post blog entries for others to read, search for other users with similar interests, and compile and share lists of contacts. In addition, user profiles often have a section dedicated to comments from friends and other users. To protect user privacy, social networks usually have controls that allow users to choose who can view their profile, contact them, add them to their list of contacts, and so on. In recent years, it has also become common for a wide variety of organizations to create profiles to advertise products and services. [2] ICT 499 CAPSTONE PROJECT 5 1.1.3.2. ADDITIONAL FEATURES Some social networks have additional features, such as the ability to create groups that share common interests or affiliations, upload or stream live videos, and hold discussions in forums. Geosocial networking co-opts internet mapping services to organize user participation around geographic features and their attributes. The new social network system designed from this project, it can link the users together using similar photos uploaded by the users, and the interesting thing is that this system can generate text information for the photos, users no need to write any information about the photos, and the system can help users to describe the photos. This feature can be done is because of the content-based image retrieval technique. Content-based image retrieval (CBIR), also known as query by image content (QBIC) and content-based visual information retrieval (CBVIR) is the application of computer vision techniques to the image retrieval problem, that is, the problem of searching for digital images in large databases. "Content-based" means that the search will analyze the actual contents of the image rather than the metadata such as keywords, tags, and/or descriptions associated with the image. The term 'content' in this context might refer to colors, shapes, textures, or any other information that can be derived from the image itself. CBIR is desirable because most web based image search engines rely purely on metadata and this produces a lot of garbage in the results. Also having humans manually enter keywords for images in a large database can be inefficient, expensive and may not capture every keyword that describes the image. Thus a system that can filter images based on their content would provide better indexing and return more accurate results. The most common method for comparing two images in content based image retrieval (typically a new uploaded image and an image from the database) is using an image distance measure. An image distance measure compares the similarity of two images in various dimensions such as color, texture, shape, and others. For example a distance of 0 signifies an exact match with the query, with respect to the dimensions that were considered. As one may intuitively gather, a value greater than 0 indicates various degrees of similarities between the images. Search results then can be sorted based on their distance to the queried image. ICT 499 CAPSTONE PROJECT 6 Color: computing distance measures based on color similarity is achieved by computing a color histogram for each image that identifies the proportion of pixels within an image holding specific values (that humans express as colors). Current research is attempting to segment color proportion by region and by spatial relationship among several color regions. Examining images based on the colors they contain is one of the most widely used techniques because it does not depend on image size or orientation. Color searches will usually involve comparing color histograms, though this is not the only technique in practice. Texture: texture measures look for visual patterns in images and how they are spatially defined. Textures are represented by texels which are then placed into a number of sets, depending on how many textures are detected in the image. These sets not only define the texture, but also where in the image the texture is located. Texture is a difficult concept to represent. The identification of specific textures in an image is achieved primarily by modeling texture as a two-dimensional gray level variation. The relative brightness of pairs of pixels is computed such that degree of contrast, regularity, coarseness and directionality may be estimated (Tamura, Mori & Yamawaki, 1978). However, the problem is in identifying patterns of co-pixel variation and associating them with particular classes of textures such as silky, or rough. Shape: shape does not refer to the shape of an image but to the shape of a particular region that is being sought out. Shapes will often be determined first applying segmentation or edge detection to an image. Other methods like [Tushabe and Wilkinson 2008] use shape filters to identify given shapes of an image. In some case accurate shape detection will require human intervention because methods like segmentation are very difficult to completely automate. [3] In the real world, man like using camera to capture his world in pictures, and conveniently share them with others. Today, more and more people become photography lovers, and share their photography work to each other, then find friends. Using the new social network service designed from this project, it is easier for them to find friends no matter how far away between each others. ICT 499 CAPSTONE PROJECT 7 1.1.3.3 PROPOSED APPROACH AND METHOD TO BE EMPLOYED FOR PROJECT In this project design approach, steps will closely following the flow chart showing below: Upload a photo To System Analyze photo Retrieve System Database Create Text Information Generate Annotation User Matching System Database Suggestion Message Display Information This project system has its own database, inside the database there are tens of thousands of photos, each photo has its own text information. When a new user uploads a photo, the system will analyze the photo based on its color, texture, shape, and other dimensions. After that system will go to its database, find out the similar photos, and compare that new photo with those similar photos based on the same dimensions (color, texture, and others), then use the similar data of those similar photos to generate a new text information for that new uploaded photo. Normally, that information can be a composite data, it comes from different photos, and the system will pick up each information from each part of the similar photos, and generate a new data. At the same time, system will save this new information as a new data into the database, so the system’s ICT 499 CAPSTONE PROJECT 8 database can keep up to date. Based on this up to date database, every photo upload to the system, can generate information for it. After system generate the information of that photo, the system will display out the information for that photo on the webpage, meanwhile, find out all the names of users who upload those similar photos, list down the users’ names with their profile photos and show on the webpage also, so that new user can know who have the similar photos, and find friend. 1.2. PROBLEM STATEMENT 1.2.1. SOFTWARE APPLICATION AND WEBPAGE INTERFACE The photo analysis process is done by the software application called MATLAB, it supports the entire data analysis process, from acquiring data from external devices and databases, through preprocessing, visualization, and numerical analysis, to producing presentationquality output, it provides interactive tools and command-line functions for data analysis operations. The photo analysis process is done through its command-line functions, and shows the result in the command-line windows. Social network system is a web based system, it does not support command-line functions for data analysis operations, and there is no direct interface from command-line output to the webpage. This issue makes difficult for the project to display the photo analysis result from command-line output in the webpage. 1.2.2. DATABASE DESIGN FOR A DYNAMIC ENVIRONMENT As mentioned above, photo analysis, search users and user data comparison are all dynamic processes, their database must be stored inside a dynamic web environment, which is server. So the problem is become where to find a server? Normally there are two ways to do that, one is to find an online space to store those data, the other way is to build my own PC as a server. For this school project, choose the suitable method to make server depends on the project’s cost and duration. 1.2.3. CHOSEN OF WEBSITE DESIGN LANGUAGE FOR A DYNAMIC ENVIRONMENT There are so many programming languages for website design, like HTML, PHP, VP.NET, JSP, and JavaScript and so on. Photo analysis, search users and user data comparison are ICT 499 CAPSTONE PROJECT 9 dynamic process, so this website design needs to choose a programming language which can support dynamic web environment. As the project duration is only one year, choose the easiest and familiar language is better for me to finish this project within schedule. CHAPTER TWO: LITERATURE REVIEW AND CITATIONS 2.1. CONTENT-BASED IMAGE RETRIEVAL TECHNOLOGY [4] 2.1.1. INTRODUCTION HE increasing amount of digitally produced images requires new methods to archive and access this data. Conventional databases allow for textual searches on meta data only. Content Based Image Retrieval (CBIR) is a technique which uses visual contents, normally called as features, to search images from large scale image databases according to users’ requests in the form of a query image [5], [12], [13]. The commercial image search engines available as on date are: QBIC, VisualSeek, Virage, Netra, PicSOM, FIRE, AltaVista, etc. Region-Based Image Retrieval (RBIR) is a promising extension of CBIR [16]. Almost all the CBIR systems designed so far widely use features like color, shape, textures, and spatial all together or few of these. For example, [6] describes a method for image retrieval purely based on color and texture. In this paper apart from the usual features like color and texture, a new feature extraction algorithm called edge histogram is introduced. Edges convey essential information to a picture and therefore can be applied to image retrieval. The edge histogram descriptor captures the spatial distribution of edges [6], [20]. Our model expects the input as Query By Example (QBE) and any combination of features can be selected for retrieval. The focus of this research is to build a universal CBIR system using low level features. These are mean, median, and standard deviation of Red, Green, and Blue channels of color histograms. Then the texture features such as contrast, energy, correlation, and homogeneity are retrieved. Finally the edge features that include five categories vertical, horizontal, 45 degree diagonal, 135 degree diagonal, and isotropic are added [6]. ICT 499 CAPSTONE PROJECT 10 2.1.2. RELATEDWORK Early work on image retrieval can be traced back to the late 1970s. In 1979, a conference on Database Techniques for Pictorial Applications was held in Florence. Since then, the application potential of image database management techniques has attracted the attention of researchers. In the early 1990s, as a result of advances in the Internet and new digital image sensor technologies, the volume of digital images produced by scientific, educational, medical, industrial, and other applications available to users increased dramatically. The difficulties faced by text-based retrieval became more and more severe. The efficient management of the rapidly expanding visual information became an urgent problem. Local features based methods proved good results [15]. For a successful CBIR, note that the indexing scheme to be efficient for searching in the image database. Recent retrieval systems have incorporated users' relevance feedback to modify the retrieval process in order to generate perceptually and semantically more meaningful retrieval results. The works shown in [6] was mixture of color, texture, and edge density for MPEG-7 standards and where as in [8] the edge histogram was used. A similar kind of approach was done in [9], [17] based on edge density for detecting people in images. In [11], [13], color and texture features were used for image retrieval. Considerable amount of work had already been done for medical images. For these types of images, texture is highly preferred [14], [21]. To make image retrieval faster, several indexing structures were designed. The most popular ones are 2D-S Tree, Graph-based, containment tree, fuzzy-based, relationship tree, etc. ICT 499 CAPSTONE PROJECT 11 2.1.3. PROPOSED CBIR MODEL The proposed CBIR framework is shown in below. The images are kept in a database called Image Database. After preprocessing, images are segmented by using the method described in [9]. Only the dominant segments are considered for feature extraction namely color histogram features, texture features, and image density features (explained in the subsequent sections). Then a single feature vector is constructed and stored in the feature database. When a query image is submitted by the user, the same work is done as explained above to get its feature vector. For similarity comparison between the query image and the database image, the Euclidean distance method is used. Using an appropriate threshold, images that are semantically closer are retrieved from the database and displayed as a thumbnail. 2.1.4. COLOR In image retrieval systems color histogram is the most commonly used feature. The main reason is that it is independent of image size and orientation. Also it is one of the most straight-forward features utilized by humans for visual recognition and discrimination. Statistically, it denotes the joint probability of the intensities of the three color channels. Once the image is segmented, from each region the color histogram is extracted. The major statistical data that are extracted are histogram mean, standard deviation, and median for each color channel i.e. Red, Green, and Blue. So totally 3 × 3 = 9 features per segment are ICT 499 CAPSTONE PROJECT 12 obtained. All the segments need not be considered, but only segments that are dominant may be considered, because this would speed up the calculation and may not significantly affect the end result. 2.1.5. TEXTURE There is no precise definition for texture. However, one can define texture as the visual patterns that have properties of homogeneity that do not result from the presence of only a single color or intensity. Texture determination is ideally suited for medical image retrievals [21]. In this work, computation of gray level cooccurrence matrix is done and from which a number of statistical measures are derived. The autocorrelation function of an image is used to quantify the regularity and the coarseness of a texture. This function is defined for an image I as: A texture is characterized by a set of values called energy, entropy, contrast, and homogeneity. The following formulas are used to calculate the features and are shown in equations 2 to 5 [22]. The performance of the texture features are tested using test images from Corel image database just like color. 2.1.6. EDGE DENSITY A novel approach in the field of image retrieval is use of edge information. The edge histogram is normally used in the area of computer vision primarily in tracking of moving objects [18]. Edges convey essential information to a picture, and their accurate detection is of primary importance. The identification of edges inside one image is the first step to recognize geometric shapes within one image [20]. ICT 499 CAPSTONE PROJECT 13 A. Edge Histogram Descriptor (EDH) The Edge Histogram Descriptor represents the local edge distribution in the image which is obtained by subdividing the whole image into 4 × 4 sub images. For each of these sub images we compute the histogram. This means a total of 16 × 5 = 80 bins are required. The histograms are categorized into four directional edges called vertical, horizontal, 45 degree, 135 degree, and one non-directional edge. To detect the edge strength, filter coefficients shown in below were applied. Edge blocks that are greater than a given threshold is selected [8]. For each sub image the edge density can be calculated using equation (6). Let (x1, y1) and (x2, y2) are the top left corner and the bottom right corner of the sub image. Then the edge density f is given by, where ar is the region area. All these features are put in the feature vector table. B. Similarity Comparison and Greedy Method For similarity comparison, we have used Euclidean distance, d using equation 7. where FQ[i] is the ith query image feature, and FDB[i] is the corresponding feature in the feature vector database. Here, N refers to the number of images in the database. The main issue in image retrieval systems is the number of dimensions of the feature vector which is normally huge. For example QBIC system reduces the 20-dimension feature vector to two or three using Principle Component Analysis (PCA) [23]. It explores exponentially with the increasing of the dimensionality and eventually reduces to sequential searching. To overcome these problems a simple method called greedy strategy is used. ICT 499 CAPSTONE PROJECT 14 Consider three database images and their corresponding segments as I1(S1, S2, S4), I2(S2, S5, S8, S7), and I3(S1). The sequence of the segments shown in I1, I2, and I3 are based on descending order of the size/area of each segment. Similarly, let QI(S7, S2) denotes the segments of the query image. The algorithm shown in below uses the greedy strategy to compare the similarity between the query image and the database images. Algorithm ImageSimilarity // I[N] – Image DB with N images // QI – Query Image foreach (Image I in I[N]) foreach (Segment s in SegmentSet) if (Euclidean(QI[s], I[s]) < threshold) // continue to check other segments else // no need to check other segments end. Suppose if we fix 20 features for each segment and if there are five segments on an average per image, then we must repeat the comparison for each segment. With this proposed method we obtain a reasonable increase in performance when the number of segments is more. 2.1.7. EXPERIMENTAL SETUP AND RESULTS A Dell Precision Pentium Core2 Duo Workstation with 2GB RAM computer is used for conducting the experiments. The main software tools used were Visual Studio 2005, C# .NET Framework for developing UI components, building the logic, etc. For the image processing work, the open source products like AForge.Imaging and AForge.Math from Google (http://code.google.com/p/aforge/) were used. To store the images and the feature vector, Oracle 10g database was selected for various reasons. Oracle Multimedia (formerly Oracle interMedia) is a feature that enables Oracle Database to store, manage, and retrieve images, audio, video, or other heterogeneous media data in an integrated fashion with other enterprise information. Corel Image database with 1000 natural images were used for testing the proposed CBIR system. ICT 499 CAPSTONE PROJECT 15 2.1.8. RETRIEVAL EFFICIENCY The retrieval efficiency, namely recall and precision were calculated using 1000 natural color images (100 in each category) from Corel image database. Below shows the screenshot of the framework. Standard formulas have been used to compute the precision and recall for four query images. By randomly selecting four query images from the CorelImage Database, the system was tested and the results are shown in Table I. ICT 499 CAPSTONE PROJECT 16 In Table I, the first line in each query image indicates precision and the second line indicates recall. Below the Table I, it shows the query images used in conducting the experiment. 2.1.9. CONCLUSION AND FUTURE WORK This paper proposed a universal model for the Content Based Image Retrieval System by combining the color, texture, and edge density features or individually. Users were given options to select the appropriate feature extraction method for best results. The advantages of global and local features together have been utilized for better retrieval efficiency. The results are quite good for most of the query images and it is possible to further improve by fine tuning the threshold and adding relevance feedback. ICT 499 CAPSTONE PROJECT 17 2.2. APACHE TOMCAT 7 [24] Apache Tomcat is an open source software implementation of the Java Servlet and JavaServer Pages technologies. The Java Servlet and JavaServer Pages specifications are developed under the Java Community Process. Apache Tomcat is developed in an open and participatory environment and released under the Apache License version 2. Apache Tomcat is intended to be a collaboration of the bestof-breed developers from around the world. The Apache Tomcat Project is proud to announce the release of version 7.0.22 of Apache Tomcat. This release includes bug fixes and new features compared to version 7.0.21 including:  Further improvements to the memory leak detection and prevention features.  Fix issue that prevented using SSL with the HTTP BIO connector and Java 7.  Add support for controlling which session attributes are replicated when using session replication (a.k.a clustering). ICT 499 CAPSTONE PROJECT 18 CHAPTER THREE: INTRUDUCTION TO SELECT OF METHODS AND ANALYSIS Based on the above Literature Research, the three main problems are motioned in “Problem Statement” can be resolved. 3.1. SOFTWARE APPLICATION AND WEBPAGE INTERFACE We can avoid direct interfacing MATLAB to Webpage, instead of the interface, before we build the website system, we will use MATLAB command-line functions to calculate all the value results of all the photos inside the photo database. When social network system analysis the photo, it can easily call out the value result from the photo result database and display on the webpage. 3.2. DATABASE DESIGN FOR A DYNAMIC ENVIRONMENT Based on the project cost and duration, Apache Tomcat is a free source software to build my PC as a server, and the setting for it is not too difficult to learn. Follow the guild line of Tomcat configuration, the database can be stored under the path: C:\Program Files\Apache Software Foundation\Tomcat 7.0\webapps\ROOT. 3.3. CHOSEN OF WEBSITE DESIGN LANGUAGE FOR A DYNAMIC ENVIRONMENT Since we use Tomcat to build the PC as a server, from the Tomcat introduction, it can support HTML, Java, and JavaScript language. Based on my study in Temasek Polytechnics, I have learnt HTML and JavaScript before, and during my study in UNISIM, I learnt Java language for 2 years. After I realise my strength for certain programming language, the best and easiest choice is to use Microsoft FrontPage to create HTML webpage, and inside the HTML code, to insert Java and JavaScript code to perform the dynamic process within the system. Using the familiar language I learnt before, can help me save more time in the project coding part, and have enough time to do the project testing and modification. ICT 499 CAPSTONE PROJECT 19 CHAPTER FOUR: DETAILED PRESENTATION OF IMAGE-BASED SOCIAL NETOWORK SYSTEM 4.1. IMAGE-BASED SOCIAL NETWORK SYSTEM PROCESS From the image-based social network system home page, there are two main function bars to go in to the users’ information pages. The first one is search user bar, you can type in users’ names and press search button, the system will display out the user’s name below, so you can click the user’s name and go in to that user’s information page. The other function bar is search photo, you can upload a photo by pressing the browse button and choose any photo you want from your local computer drive, then the photo will display under below, when you click that photo, the system will display out a user list with all the users whose favourite photos are related to that photo you upload one, so you can click each user’s name and go in to see the user details. ICT 499 CAPSTONE PROJECT 20 4.1.1. SEARCH BY USERS The search user by name process can be described as below flow chart: From the image-based social network home page, type in the user name “liu yue” in the search function bar, and click search. The system will go to its user database and find the user’s name match the words you type in, and display out the result – the user page link. ICT 499 CAPSTONE PROJECT 21 If you only type “liu” in the search function bar, the system will go to its user database find out all the users’ name contain word “liu”, and display out those users’ names. After the user page link displays under the search function bar, you can either click the user name go in to the user page or follow the path address the result tell you, copy and paste it to the internet browser to see the user page. This search engine function is using JavaScript programming code (Refer to Appendix – Home Page Coding) to execute the search process, the good reason to use JavaScript programming code for this function part is JavaScript code can be used for the dynamic process, but HTML code cannot perform the dynamic function, and JavaScript can insert into HTML code and perform the dynamic process function. ICT 499 CAPSTONE PROJECT 22 Using user Liu Yue his user page as an example It is a standard social network user information page, inside has the information about user profile picture, the user name, date of birth, nationality, occupation, hobby, email and his favourite photos upload by him. At the bottom of this user page, there is a link “Go back to Home Page” can bring you go back the image-based social network system home page, and search for other users. Beside the user’s profile picture, there is a button “Find My Neighbours”, if you click this button, it will go to system database, using user Liu Yue’s all the information, text information and photo information to compare with other users’ text information and photo information. The system will analyze all the information, including comparison for all the same category text information, like comparison for user name, user date of birth, user hobby and so on...and give all the same category comparison results a similarity number. At the same time, it also perform the photo analysis process using Content-based image retrieval technology (Refer to Literature Review and Citations, Content-based image retrieval technology) to analyze all the photo from user liu yue, and use them to compare with all the photos which other users have, it separates those photos into different ICT 499 CAPSTONE PROJECT 23 categories, for example, those photos related to basketball will compare with each other, those photos related to dance will compare with other dance photos. Based on the formula of Literature Review and Citations - Content-based image retrieval technology, it calculates the value base on the color, texture and edge density of those photos, and displays the result value to give different category photos comparison a result number – the similarity percentage number. At the last, the system will collect all the similarity percentage numbers from photo analysis and text information analysis, and calculate the average similarity percentage number and send back it to user Liu Yue. So when you click “Find My Neighbours”, it will display out three users’ name beside the Liu Yue’s profile picture, and show the similarity number for each users. The three users are the top three users who are more similar to user Liu Yue comparing with other users in the social network system. After the top three similar users come out, you can click each user’s name to see their user information pages, or if you want to see why these three users are the most similar users to user Liu Yue, you can click the similarity number, and it will bring you to another page – the user comparison page. ICT 499 CAPSTONE PROJECT 24 In the user comparison page, it will list out all the comparison results between every same category information, like above example, the similarity for Liu Yue’s date of birth and Luo Zhi Xiang’s date of birth is 60%, because they have the same month – September, but the date and year are different, so the similarity is only 60%. And the nationality is the same – China, so this part the similarity is 100%, follow by Hobby, photo. The photo part also displays the similarity result for the same category photo, like Bruce Lee, the two photo are all related to the same person – Kong Fu star Bruce Lee, they are playing shuang jie gun, but they are different person, first one is Liu Yue, he just learnt from Bruce Lee to play shuang jie gun, and the second one is the person Bruce Lee, so the similarity for these two photo is 60%, below table shows the detail for using Content-based image retrieval technology to calculate the similarity: Bruce Lee Photo User Liu Yue User Luo Zhi Xiang ICT 499 CAPSTONE PROJECT Color 10 60 Texture 50 50 EHD 30 40 Average Results 30 50 similarity (30/50)x100% = 60% 25 Base on the Content-based image retrieval concept, the system can calculate the similarity results for Dancing and Basketball photos. If user Luo Zhi Xiang wants to be a friend with Liu Yue, he just clicks “Liu Yue” or Liu Yue’s profile picture to go to Liu Yue’s user information page, looks for Liu Yue’s email address and writes email to him to contact him. At the bottom of this page, there is also a link “Go back find other users” can go back to the social network system home page to find other users you want to look for. 4.1.2. SEARCH BY PHOTO Search user is a very common method to look for user in many social network systems, but this image-based social network system has a very special method to look for user, that is search user by upload photo. The process will follow the below flow chart: ICT 499 CAPSTONE PROJECT 26 From the image-based social network system home page, under the search photo function bar, when you click “Browse” button, it will open a new small window and ask you to choose a photo from your local computer drive and open it, after you open the photo it will display out below the search photo function bar. Using above screen shot as a detail example, when you upload a Michael Jordan’s photo, the Michael Jordan’s photo will display below this search photo function bar, and when you click this Michael Jordan photo, it will bring you go to another webpage – photo search result page with a user list and all the similar photos each individual user has, and you can click each individual user’s name or profile picture to go to each individual user’s information page and check that user’s favourite photos, inside those favourite photos, you sure can find some photos which are similar to Michael Jordan’s photo. ICT 499 CAPSTONE PROJECT 27 Photo 1 Photo 2 Photo 3 Photo 1 Photo 2 This process is using Content-based image retrieval technology (Refer to Literature Review and Citations, Content-based image retrieval technology) to perform it, and the programming language is using HTML plus JavaScript. When you upload a Michael Jordan’s photo, this image-based social network system will analyze the Michael Jordan’s photo, after the analysis finish, it will go to its photo database to search for all the photos are similar to this Michael Jordan’s photo, and use the Content-based image retrieval technology formula based on the color, texture and edge density to calculate the similarity value for each photo those are similar to the Michael Jordan’s photo. After the similarity ICT 499 CAPSTONE PROJECT 28 calculation finish, the system will use these similar photos to search for their owners from user database, and list out all the users’ name with their photo together to show on the photo search result page. As above example, user Liu Yue has the same photo as the Michael Jordan’s photo, so the similarity value is 100%, for user Luo Zhi Xiang, it’s the same, and he also has the same photo as Michael Jordan’s photo, the similarity value is 100% too. And for user Michael Jordan, he is the owner of that Michael Jordan’s photo, so the system will list out all the photos he has, because those photos are all about himself – Michael Jordan, and also show out the similarity results for each photo he has. The last user has the similar photo to Michael Jordan is Lebron James, he does not have Michael Jordan’s photo, but his photo are all about sports – basketball, same activity as user Michael Jordan’s photo, and his basketball clothes number is 23, same as Michael Jordan’s, some clothes is the same color with Jordan’s clothes and so on...base on these color, activity, texture information, the image-based social network system can give each photo a similarity value, and displays out. Using below table to show the detail for Contentbased image retrieval calculation of each similarity result, this can make user understand clearly what’s the reason each photo has the similar part to that Michael Jordan’s photo. Michael Jordan Photo User Jordan User Lebron photo 1 User Lebron photo 2 Color 70 75 10 Texture 80 30 50 EHD 60 42 66 Average Results 70 49 42 similarity (49/70)x100% = 70% (42/70)x100% = 60% Base on the search user by upload photo flow chart, the following process will be the same as search user by name, after user finish the photo search result page, they can go to each user’s information page by click that user’s name or profile picture, when go to each user’s main page, to click “Find My Neighbours” to search for the person who is more closed to them. ICT 499 CAPSTONE PROJECT 29 4.2. PROJECT MANAGEMENT During the study in SIM University, IT Project Management course helps me to gain insight knowledge on the good practices one should have when executing project management. Using the IT Project Management knowledge tools such as Work Breakdown Structure (WBS), project management lifecycle, risk assessment and development of schedules (project Gantt chart) help me to manage well my image-based social network system project. 4.2.1. PROJECT MANAGEMENT LIFECYCLE The following is the phase of the project management lifecycle that was being read on: Project Initiation Project Planning Project Execution & Control Closing Project Project Initiation: understand well the scope of the project and work with my supervisor closely to think about the idea to achieve the project goal. Project Planning: separate the project into different phases, set a task for each phase, and the planning to finish each task. Project Execution & Control: follow the project planning to finish every task during each project phase, perform testing at the end of every phase, and prepare risk management to control the project to avoid the project goes to the wrong direction. Closing Project: perform project modification to check whether the output of the project meet the project goal, and prepare final project report. ICT 499 CAPSTONE PROJECT 30 4.2.2. WORK BREAKDOWN STRUCTURE (WBS) This project follows the below Work Breakdown Structure (WBS) strictly to finish every task during each project phase: ICT 499 CAPSTONE PROJECT 31 4.2.3. PROJECT GANTT CHART In order to achieve the project objective successfully, proper project schedule plan is required. Project Gantt chart is the most useful tool to monitor the project schedule and process, below is the project Gantt chart for the image-based social network system project: 4.2.4. RISK ASSESSMENT 4.2.4.1. RISK IDENTIFICATION Before starting doing this image-based social network system project, it is necessary to identify the risk hiding in the project period, analyze the impact of the risk, so we can have enough preparation to avoid the project goes to the wrong direction. 1). Knowledge of media engineering: as an IT student, I did not have any knowledge of media process and Content-based image retrieval technology. It is my first time to touch with any media engineering field, especially to use IT programming to perform media process. I may be confused in some understanding of the image retrieve process, and it may cause to use it wrongly to create the project, in the end make the project fail. ICT 499 CAPSTONE PROJECT 32 2). Full time work and part time study: I’m a full time worker, and part time student, there will be a conflict problem between working and school project doing, sometimes working time may affect the school project schedule and cause there is not enough time to self-study of media process and Content-based image retrieval technology, and finally cause the school project cannot be finished within dateline. 4.2.4.2. MITIGATION STEPS 1). Follow the project Gantt chart strictly, in order to ensure this image-based social network system project can be successfully completed within dateline. 2). Understand the Content-based image retrieval technology very well, before using it to integrate with IT programming part to perform media process, in order to avoid unnecessary mistakes during project. 3). Plan well the time for project doing and working, make sure they will not interrupt each other. CHAPTER FIVE: EVALUATION OF EXPERIMENTAL/THEORETICAL RESULTS Finally, we can start a complete testing for this image-based social network system. We open a Internet Explorer, type in the social network address: http://localhost:8080/index.htm to open it. First, we start to test the “Search by Users” function, we type in “SIM” in the search by users function bar, and click “Search” button, then below the search by users function bar shows “Search Results, Total found: 0”, it means there is no “SIM” user, to check whether it is correct, we go to user database “C:\Users\1\Documents\Project”, there is no “SIM” user inside, so this search user engine works good, it can indicate the error message to tell people whether a user is inside the social network or not. Now we type in “Liu Yue” in the search by users function bar, and click “Search” button, then below shows “Liu Yue Score: 14, Click the user's name or use below path to see the details, http://localhost:8080/LiuUser.htm, Total found: 1”, and we click “Liu Yue”, it brings us to user Liu Yue’s user page. Inside the Liu Yue user page, we can see the information about ICT 499 CAPSTONE PROJECT 33 Liu Yue and his favourite photo, near Liu Yue’s profile picture, we click “Find My Neighbours”, it comes out three users – Luo Zhi Xiang, Lee Hyori and Park Ji Yoon, beside each users’ picture, there is a black color bar and a number, Luo Zhi Xiang – 80%, Lee Hyori – 70%, Park Ji Yoon – 70%, what’s this mean? So we just click “80%”, it goes to another new page, from this new page, we can clearly know this 80% is the similarity number that indicates how much percentage user Luo Zhi Xiang is similar to user Liu Yue, and this page also lists out for every same category information between these two users the comparison result value, like they have 60% similarity in the date of birth, 100% similarity in the nationality, 80% similarity in hobby, 60% similarity in the photo they upload about Bruce Lee, 60% similarity in the dancing photo and so on...so the total average similarity for these two users is 80%. At the bottom of this page, we click “Go back find other users”, so we go back to the social network home page. We have finished testing for “Search By Users”, now we move to “Search By Photo”. We click “Browse” and a small window comes out, we choose a photo of Michael Jordan from my pictures, when we select the photo and click open, the photo of Michael Jordan will display under the Search by Photo function bar, and we click this photo, it will bring us to a new page, the search photo result page. Inside this search photo result page, there are many users come out with their profile pictures, and on the right side of every users, there are some photo with numbers, these numbers are the similarity value to indicate how similar each photo from each user is to that photo of Michael Jordan, and if you click each user’s name, it will go to each user’s user page. The complete testing is finished, there is no problem for the working process of imagebased social network system. CHAPTER SIX: CONCLUSIONS AND REFLECTIONS 6.1. CONCLUSIONS Social network service is a very popular social media tool in the world, more and more new ideas have been created and put into the social network to make this service become a very powerful search engine to connect people together in every place of the world. For example, Facebook is the most popular social network service in the recent world, it can connect people base on their sharing information, like city, hobby, nationality and photos. It ICT 499 CAPSTONE PROJECT 34 analyzes users’ photos base on the people’s face images in every photo users have, in order to generate the users’ names from their face images, and using the names text information to link people together. Using Content-based image retrieval technology for creating an image-based social network is a new generation social network service, it has more powerful features than Facebook, Facebook has the limitation for analysis photo, which is it only can do analysis base on people’s faces, but for image-based social network, there is no limitation for its photo analysis function, it can analysis photo base on the photo’s color, texture and edge density, and transfer the analysis result into numbers, so this system can link people together base on the similar analysis result numbers. This image-based social network project is a real challenge for me, because it is not only a complete design of website, but also a big integration project, which is the integration between IT programming and media process. I’m an IT student, most of the IT knowledge I learnt from school is about programming, coding and object-oriented system design, I did not have any knowledge about media process, especially Content-based image retrieval technology. So I have to start with learning the process of Content-based image retrieval, and how it works. After a complete understanding of Content-based image retrieval technology, I need to think about a solution for how to build a platform to perform the process of Content-based image retrieval, and finally to convert it into the programming code to execute the process working. After a long time discussing with my supervisor, finally we think about a solution to perform the image retrieval process, which is to build a website as a platform to let the image retrieval process working on it. So base on the solution we think out we design the image-based social network website, and use dynamic programming language to perform the image retrieval work flow. After a complete testing for the whole system, finally the image-based social network system project was done. What I learnt from this project experience is the way we think about the connection between two things, and how to integrate the two things. From this social network project, we cannot make us focus on the image retrieval itself, we must think out of box, what is the work flow for image retrieval process? Can we use some thing we familiar with to perform that work flow process? What kind of platform I need to build to let the image retrieval process perform? Base on these questions, we can think out a solution to implement the integration for IT and Image Retrieval. ICT 499 CAPSTONE PROJECT 35 6.2. REFLECTIONS There are two incomplete parts for this image-based social network system to be improved. The first part is all the photo analysis results are saved inside the social network system database directly, so the system can call out the data base on the photo upload. The working process of the photo analysis is not done inside the system, it is done by the software called MATLAB, this MATLAB analyzes each photo and generates the results, and the social network system records the results and saves them inside its database. Because I have spent too much time on the system function design and system integration part, in order to finish the project on time, in the end there is no time for me to think about a design for the integration part for MATLAB and Website, if this design can be added into the system, it will make the social network system become more perfect. The second incomplete part is the design for the whole social network system is simple, include the background, user page, homepage, it is not attractive and colorful enough. This is also due to I spent too much time for the system function design and system integration part. The purpose of this project is to demo a new concept for the social network using image-based function to link people, it is the trend of social network service development. If the webpage design is more attractive and colorful, it will help to attract more people’s attention. Base on the two incomplete parts of the project, I understand that project modification is also an important part of the whole project, it will help to improve the project to become more prefect. I should plan more time on the project modification part in the project schedule, this also reflects my project management skill’s weakness, and I did not plan well for the project schedule. I will do more practices on the project management during my future job and study journey, in order to improve my project management skill and accumulate my project management experience. ICT 499 CAPSTONE PROJECT 36 REFERENCES [1] Social Network - Wikipedia, the free encyclopaedia, October 2011, http://en.wikipedia.org/wiki/Social_network [2] Social network service – Wikipedia, the free encyclopedia, 25th Feb 2011, http://en.wikipedia.org/wiki/Social_network_service [3] Content-based image retrieval – Wikipedia, the free encyclopedia, 10th Jan 2011, http://en.wikipedia.org/wiki/Content-based_image_retrieval [4] S. Nandagopalan, Dr. B. S. Adiga, and N. Deepak, A Universal Model for ContentBased Image Retrieval, World Academy of Science, Engineering and Technology 46 2008 [5] Tristan Glatard, John Montagnat, “Texture based Medical image indexing and retrieval: application to cardiac images". [6] B. S. Manjunath, Jens-Rainer Ohm, Vinod V. Vasudevan, and Akio Yamada, "Color and Texture Descriptors". In: IEEE Transactions on Circuits and Systems for Video Technology, Vol. 11, No. 6, June 2001, pp. 70-715. [7] Zhe-Ming Lu1, Su-Zhi Li, and Hans Burkhardt, "A Content-Based Image Retrieval Scheme in JPEG Compressed Domain", International Journal of Innovative Computing, Information and Control ICIC International 2006 ISSN 1349-4198, Volume 2, Number 4, August 2006, pp. 831-839. [8] Minyoung Eom, and Yoonsik Choe, "Fast Extraction of Edge Histogram in DCT Domain based on MPEG7", Proceedings of World Academy of Science, Engineering and Technology Volume 9 November 2005 ISSN 1307-6884, pp. 209-212. [9] Son Lam Phung and Abdesselam Bouzerdoum, "A New Image Feature for Fast Detection of People in Images", International Journal of 2007 Institute for Scientific Information and Systems Sciences Computing and Information Volume 3, Number 3, pp. 383-391. [10] Paul Stefan, et al.: Segmentation of Natural Images for CBIR.C. J. Kaufman, Rocky Mountain Research Lab., Boulder, CO, private communication, May 1995. [11] P. S. Hiremath , Jagadeesh Pujari, "Content Based Image Retrieval using Color, Texture and Shape features", 15th International Conference on Advanced Computing and Communications, IEEE Computer Society 2007, pp. 780-784. ICT 499 CAPSTONE PROJECT 37 [12] Remco C. Veltcamp, Mirela Tanse, "Content Based Image Retrieval Systems". A Survey, Technical Report UU-CS-2000-34, October 2000, pp. 1-62. [13] Mustafa Ozden and Ediz Polat, "Image Segmentation using Color and Texture features". [14] John Montagnat, et al, "Texture-based Medical Image Indexing and Retrieval on Grids", Medical Imaging technology, vol 25 No. 5 Nov 2007. pp. 333-338. [15] C. R. Shyu, et. al, "Local versus Global Features for Content-Based Image Retrieval", IEEE Workshop on Content-Based Access of Image and Video Libraries, 1998. [16] Roger Weber and Michael Mlivoncic, "Efficient Region-Based Image Retrieval", ACM CIKM '03 November 3-8, 2003, USA. [17] S. L. Phung and A. Bouzerdoum, "Detecting People in Images: An Edge Density Approach", IEEE, ICASSP 2007. pp. 1229-1232. [18] Bohyung Han, Changjiang Yang, et al, "Bayesian Filtering and Integral Image for Visual Tracking". [19] Vincent Arvis, Christophe Debain, et. al, "Generalization of the Cooccurrence Matrix for Color Images Application to Color Texture Classification", Image AnalStereol 2004, pp. 63-72. [20] Alberto Amato, Vincenzo Di Lecce, "Edge Detection Techniques in Image Retrieval: The Semantic Meaning of Edge", 4th EURASIP Conference on Video/Image Processing and Multimedia Communications, Zagreb, Croatia. pp. 143-148. [21] Thomas M. Lehmann, et al, "Automatic categorization of medical images for contentbased retrieval and data mining", Computerized Medical Imaging and Graphics. Elsevier 2004. pp. 143-155. [22] Dong Yin, Jia Pan, et al, "Medical Image Categorization based on Gaussian Mixture Model", IEEE 2008 International Conference on BioMedical Engineering and Informatics, pp. 128-131. [23] Dr. Fuhui Long, Dr. Hongjiang Zhang and Prof. David Dagan Feng, "Fundamentals of Content-Based Image Retrieval” - http://research.microsoft.com/asia/dload_files/group/mcomputing/2003P/ch01_Long_ v40-proof.pdf [24] Apache Tomcat – Welcome, http://tomcat.apache.org ICT 499 CAPSTONE PROJECT 38 APPENDIX Home Page Code: <html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoftcom:office:office" xmlns="http://www.w3.org/TR/REC-html40"> <head> <meta http-equiv="Content-Type" content="text/html; charset=windows-1252"> <link rel="File-List" href="index.files/filelist.xml"> <title>Home Page</title> <SCRIPT LANGUAGE="JavaScript">      </script> <script type="text/javascript">  </script>  </head> <body bgcolor="#FFFF99" style="background-attachment: fixed"> <a href="http://www.unisim.edu.sg/"><img border="0" src="SIM-logo.gif" width="253" height="121"></a>     <![if !vml]><img border=0 width=1164 height=58 src="index.files/image001.gif" alt="Welcome to SIM Social Network" v:shapes="_x0000_s1025"><![endif]> ICT 499 CAPSTONE PROJECT 42             &nbsp ;             & nbsp;            &nb sp;            &nbsp ;             & nbsp;            &nb sp;            &nbsp ;             & nbsp;     <center> Search By Users:    <tr><td> <input type=text name=srchval id=srchval value="" size="34"><input type=button onclick="javascript:search(document.getElementById('srchval').value);" value="Search"></td></tr> <div id="search1" name="search1"> </div> <div align="center" style="line-height: 1.9em;">    Seach By Photo: <input type="file" id="picField" onchange="preview(this)" size="41"> <a id="linkField2"><img alt="Graphic will preview here" id="previewField" src="blank.jpg"></a> </center>       </body> </html> ICT 499 CAPSTONE PROJECT 43 User Page Code: <html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoftcom:office:office" xmlns="http://www.w3.org/TR/REC-html40"> <head> <meta http-equiv="Content-Language" content="en"> <meta http-equiv="Content-Type" content="text/html; charset=windows-1252"> <link rel="File-List" href="LiuUser.files/filelist.xml"> <title>LiuYue Page</title>  </head> <body bgcolor="#FFFF99" style="background-attachment: fixed"> <form action="LiuUserNB.htm"><img border="0" src="LiuYue.jpg" width="139" height="159">           & nbsp;            &nb sp;            &nbsp ;     <input type="submit" value="Find My Neighbors"> </form> Name: Liu Yue Date of Birth: 02 Sep 1983 Nationality: China <![if !vml]><img width=202 height=40 src="LiuUser.files/image001.gif" v:shapes="_x0000_s1025"><![endif]>Occupation: Student <![if !vml]><img width=108 height=40 src="LiuUser.files/image002.gif" v:shapes="_x0000_s1026"><![endif]>Hobby: Hip pop music, dance, sing songs, basketball Email: <a href="mailto:liuyue15@hotmail.com">liuyue15@hotmail.com</a> Favorite Photo: <img border="0" src="ysj05.bmp" width="404" height="286">   <img border="0" src="ysj04.jpg" width="196" height="286">   <img border="0" src="ysj03.jpg" width="207" height="286">   <![if !vml]><img width=327 height=49 src="LiuUser.files/image003.gif" v:shapes="_x0000_s1027"><![endif]> <img border="0" src="ysj02.jpg" width="192" height="287">   <img border="0" src="ysj01.jpg" width="380" height="284">   <img border="0" src="ysj06.jpg" width="191" height="285">   <img border="0" src="lj02.jpg" width="376" height="282">   <img border="0" src="Michael-Jordan.jpg" width="230" height="280"><![if !vml]><img width=327 height=49 src="LiuUser.files/image004.gif" v:shapes="_x0000_s1028"><![endif]><![if !vml]><img width=327 height=49 src="LiuUser.files/image005.gif" v:shapes="_x0000_s1029"><![endif]>     <a href="index.htm">Go back to Home Page</a> </body> </html> <html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoftcom:office:office" xmlns="http://www.w3.org/TR/REC-html40"> <head> <meta http-equiv="Content-Language" content="en"> <meta http-equiv="Content-Type" content="text/html; charset=windows-1252"> <link rel="File-List" href="ZhuUser.files/filelist.xml"> <title>Luo Zhi Xiang</title>  </head> <body bgcolor="#FFFF99" style="background-attachment: fixed"> <form action="ZhuNBUser.htm"> <img border="0" src="XiaoZhu.jpg" width="184" height="238">           & nbsp;            &nb sp;            &nbsp ;     <input type="submit" value="Find My Neighbors"> </form> Name: Luo Zhi Xiang Date of Birth: 23 Sep 1976 Nationality: Chinese Occupation: Singer, Actor Hobby: Basketball, football, swimming, hip pop music, dance, sing songs email: <a href="mailto:zhixiang@hotmail.com">zhixiang@hotmail.com</a> Favorite photo: <img border="0" src="xz02.jpg" width="242" height="360">   <img border="0" src="xz01.jpg" width="282" height="360">   <img border="0" src="xz06.jpg" width="248" height="361">   <![if !vml]><img width=222 height=83 src="ZhuUser.files/image001.gif" v:shapes="_x0000_s1026"><![endif]> <![if !vml]><img width=165 height=65 src="ZhuUser.files/image002.gif" v:shapes="_x0000_s1025"><![endif]> <img border="0" src="xz05.jpg" width="248" height="353">  <img border="0" src="xz04.jpg" width="291" height="353">   <img border="0" src="xz03.jpg" width="239" height="355">  <img border="0" src="lj02.jpg" width="376" height="282">  <img border="0" src="Michael-Jordan.jpg" width="230" height="280"> <![if !vml]><img width=327 height=49 src="ZhuUser.files/image003.gif" v:shapes="_x0000_s1028"><![endif]> <![if !vml]><img ICT 499 CAPSTONE PROJECT 47 width=327 height=49 src="ZhuUser.files/image004.gif" v:shapes="_x0000_s1029"><![endif]> <![if !vml]><img width=233 height=67 src="ZhuUser.files/image005.gif" v:shapes="_x0000_s1027"><![endif]>   <a href="index.htm">Go back to Home Page</a> </body> </html> <html xmlns:v="urn:schemas-microsoft-com:vml" xmlns:o="urn:schemas-microsoftcom:office:office" xmlns="http://www.w3.org/TR/REC-html40"> <head> <meta http-equiv="Content-Language" content="en"> <meta http-equiv="Content-Type" content="text/html; charset=windows-1252"> <link rel="File-List" href="LiuPKZhu.files/filelist.xml"> <title>Liu Yue Compared With Luo Zhi Xiang</title>  </head> <body bgcolor="#FFFF99" style="background-attachment: fixed">             &nbsp ;             & nbsp;            &nb sp;            &nbsp ;             & nbsp;       <![if !vml]><img border=0 width=413 height=58 src="LiuPKZhu.files/image001.gif" alt="SIMILARITY = 80%" v:shapes="_x0000_s1028"><![endif]> <![if !vml]><img width=146 height=88 src="LiuPKZhu.files/image002.gif" v:shapes="_x0000_s1026"><![endif]>      &nb sp;            &nbsp ;             & nbsp;            &nb sp;           <a href="LiuUser.htm"><img border="0" src="ysj0.jpg" width="124" height="142"></a>          &nb sp;            &nbsp ;             & nbsp;            &nb sp;            &nbsp ICT 499 CAPSTONE PROJECT 49 ;             & nbsp; <a href="ZhuUser.htm"><img border="0" src="xz0.JPG" width="119" height="153"></a>   <![if !vml]><img width=186 height=88 src="LiuPKZhu.files/image003.gif" v:shapes="_x0000_s1027"><![endif]>                     Date of Birth:    02 Sep 1983            &nbs p;                           &n bsp;           23 Sep 1976         --------------------------    60%                &nbsp ;    Nationality:        China         &nbsp ;             & nbsp;            &nb sp;            &nbsp ;             & nbsp;            &nb sp;             --------------------------    100%            &nbsp ;    Hobby:        & nbsp;      Basketball, hip pop music, dance, sing songs            &nb sp;            &nbsp ;   --------------------------    80%            &nbsp ;    Photo:             &nbsp ;  ICT 499 CAPSTONE PROJECT 50 <img border="0" src="ysj01.jpg" width="309" height="231">   <img border="0" src="xz01.jpg" width="179" height="232">   Bruce Lee            &nbsp ;             & nbsp;  -------------------------    60%           <img border="0" src="ysj04.jpg" width="159" height="232">   <img border="0" src="xz02.jpg" width="157" height="231">  Dancing            & nbsp;            &nb sp;            &nbsp ;             & nbsp;         -------------------------    60%           <img border="0" src="lj02.jpg" width="322" height="240">  Lebron James            &nb sp;            &nbsp ;             & nbsp;             --------------------------    100%           <img border="0" src="Michael-Jordan.jpg" width="188" height="234">  Micheal Jordan            &n bsp;            &nbs p;                           &n bsp;            &nbs p;       -------------------------    100%           <img border="0" src="ysj05.bmp" width="294" height="209">  <img border="0" src="xz03.jpg" width="143" height="209">  Basketball          & nbsp;                   &nbsp ;     ---------------------------    80%   <a href="index.htm">Go back find other users</a>   </body> </html> ICT 499 CAPSTONE PROJECT 51 <html> <head> <meta http-equiv="Content-Language" content="en"> <meta http-equiv="Content-Type" content="text/html; charset=gb2312"> <title>Jordan Photo Result</title> </head> <body bgcolor="#FFFF99" style="background-attachment: fixed"> <a href="LiuUser.htm"> <img border="0" src="LiuYue.jpg" width="111" height="133"></a> <a href="LiuUser.htm">Liu Yue</a>         -----------------<img border="0" src="Michael-Jordan.jpg" width="112" height="137"> 100% <a href="ZhuUser.htm"> <img border="0" src="XiaoZhu.jpg" width="113" height="141"></a> <a href="ZhuUser.htm">Luo Zhi Xiang</a>      -----------------<img border="0" src="Michael-Jordan.jpg" width="112" height="137"> 100% <a href="JordanUser.htm"> <img border="0" src="Michael-Jordan.jpg" width="112" height="134"></a> <a href="JordanUser.htm">Micheal Jordan</a>      ----------------<img border="0" src="Michael-Jordan.jpg" width="112" height="137"> 100%            &n bsp;            &nbs p;   ----------------<img border="0" src="mj01.jpg" width="116" height="147"> 60%            &n bsp;            &nbs p;   ----------------<img border="0" src="mj02.jpg" width="191" height="141"> 50% <a href="JamesUser.htm"> <img border="0" src="lebron-james.jpg" width="123" height="119"></a> <a href="JamesUser.htm">Lebron James</a>      ---------------<img border="0" src="lj02.jpg" width="201" height="152"> 70%            &n bsp;            &nbs p;  ----------------<img border="0" src="lj03.jpg" width="202" height="151"> 60%            &n bsp;            &nbs p;   ICT 499 CAPSTONE PROJECT 52 ----------------<img border="0" src="mj02.jpg" width="208" height="153"> 50% <a href="index.htm">Go back to home page</a> </body> </html> ICT 499 CAPSTONE PROJECT 53

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