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Image Tagging With Social Assistance

International Journal of Trend in Scientific
Research and Development (IJTSRD)
Special Issue – Active Galaxy
ISSN No: 2456 - 6470 | www.ijtsrd.com | Special Issue Publication
Image Tagging With Social Assistance
D. Dhayalan1, M. Queen Mary Vidya2, B. Gowri Priya3
Assistant Professor, 3PG Student
Department of Master
aster of Computer Applications 1,3, Department of Computer Science and Engineering2
Veltech Hightech Dr Rangarajan Dr Sakunthala Engineering College
Avadi, Chennai, India
Nowadays Social Media focus on users to billions of
images, famous
e commerce web sites such as
Flipkart, Amazon etc. Tag-primarily
primarily based definitely
image are seeking for is an essential method to find
photos shared with the aid of manner of clients in
social networks. But, a manner to make the top ranked
result applicable and with range is difficult. In this
paper, we advocate a topic diverse ranking method for
primarily based photo retrieval with the eye of
selling the situation insurance overall performance.
First, we bring together a tag graph based totally
absolutely at the similarity among every tag. Then
network detection approach is accomplished to mine
the subject community of every tag. After that, inter
intercommunity and intra-network
network ranking are added to
gather the very last retrieved results. Inside the inter
internetwork rating way, an adaptive random stroll model
is employed to rank the network primarily based at
the multi-information
information of every topic community
Keywords: Image based image retrieval Iterative
algorithm, Re-ranking
1. Introduction
Generally, tag-based
based or Meta information based
search is first applied to obtain the initial set of result
from a large text-indexed
indexed image database. Then the
top returned images are reordered via various re
reranking approaches by mining
ning the visual patterns in
the images. Re-Ranking
Ranking means we have go search for
the Google images they have based on automation we
have to use algorithm and then analyse the particular
pictures. And we have to research the pictures they
are related images
mages will be shown .They are peoples
should be the tag for the pictures will be count on the
Now days, many innovative web search optimization
techniques have been developed worldwide and used
by search engines like yahoo, google etc. to get the
most relevant information related to users’ queries.
The images which are present in social multimedia
networking websites are accompanied by different
tags, comments, annotations and other related
information. Images in E-Commerce
Commerce web sites like
amazon, f1ipkart etc. Are accompanied by
information such as links, producer and consumer
related information, comments and annotations.
Images in such type websites accompanied by all the
links, annotations, comments and tags form an image
rich information networks
rks . tag-based
social image
search methods cannot achieve satisfactory results for
two reasons. First, there is too much noise in useruser
provided tags. Many tags are irrelevant or incorrectly
spelled. There are two ranking options for tag-based
social image search, namely, time-based
ranking and
based ranking. The time-based
method ranks images based on the uploading time of
each image, and the interestingness-based
method ranks images based on each image’s
involvement in Flickr.
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Special Issue – Active Galaxy
Fig 1: Retrieved image count statistics for query
2.1 Learning Tag Relevance by Neighbor Voting
for Social Image Retrieval
Social image retrieval is significant for exploiting the
multimedia such as Flickr images. Since
inexperienced tagging is known to be uncontrolled, to
the uncertain, and personalized, a basic problem is
how to reliably interpret the application of a tag with
respect to the visual content it is describing. In many
cases, if different persons label similar images using
the same tags, these tags are likely to reflect objective
aspects of the visual content. Starting from this
understanding, we propose a novel algorithm that
measurability and reliably learn tag relevance by
accumulating votes from visually similar neighbors.
Additionally treated as tag frequency, learned tag
relevance is seamlessly embedded into current tagbased social image retrieval paradigms. Preliminary
experiments on one million Flickr images display the
potential of the proposed algorithm. General
comparisons for both single-word queries and
multiple-word queries show substantial improvement
over the baseline by learning and using tag relevance.
Specifically, compared with the baseline using the
original tags, on average, recovery using enhanced
tags increases means average precision by 24%, from
0.54 to 0.67. Moreover, simulated experiments
indicate that performance can be improved further by
scaling up the amount of images used in the proposed
neighbor voting algorithm.
2.2 Tag-Based Social Image Search: Toward
Relevant and Diverse Results
Recent years have witnessed a great success of social
media web sites. Tag-based image search is an
important approach to access the image content of
interest on these websites. However, the existing
ranking methods for tag-based image search regularly
return results that are irrelevant or lack of diversity.
This chapter presents a diverse application ranking
scheme which simultaneously takes application and
diversity into account by exploring the content of
images and their associated tags. First, it estimates the
relevance scores of images with respect to the query
term based on both visual information of images and
semantic information of associated tags. Then
semantic comparisons of social images are estimated
based on their tags. Based on the application scores
and the similarities, the ranking list is produced by a
selfish ordering algorithm which optimizes Average
Diverse Precision (ADP),a novel measure that is
extended from the conventional Average Precision
(AP).Comprehensive experiments and user studies
demonstrate the effectiveness of the approach.
2.3 Optimized Hyper graph Based Social Image
Search Using Visual-Textual Joint Relevance
Recent years have witnessed a great success of social
media websites. Tag-based image search is an
important approach to access the image content of
interest on these websites. However, the existing
ranking methods for tag-based image search
frequently return results that are irrelevant or lacking
in diversity. Most of the existing methods estimate the
relevance of images by using tags and visual
characteristics either separately or sequentially. The
proposed system uses an approach that utilizes
together both visual information and textual
information in real time to estimate the application of
user tagged image. The method used to determine the
relevance estimation is the hyper graph learning
approach. The hyper graph is a generalization of a
graph in which an edge in the hyper graph can be
connected to any number of vertices. In the proposed
method each social image can be represented by the
bag-of-visual words and bag-of-textual words, which
can be obtained from the textual content and visual
content of the particular image. A hyper graph can be
constructed in which the vertices represent the social
images for ranking and the each hyper edge represents
the visual words or tags that are obtained from the
image. In the hyper graph learning scheme, both the
visual content and tag information are taken into
consideration at same time. Different from the method
used by the traditional hyper graph, in the proposed
system a social image hyper graph is constructed
where vertices represent the images and hyper edges
represent the visual or textual terms. The set of
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Special Issue – Active Galaxy
pseudo-positive images are used to achieve the
learning, where the weight of hyper edges is updated
throughout the learning process. Thus only the most
relevant images are given to the user.
2.4 Diversifying the Image Retrieval Results
In the area of image retrieval, post-retrieval
processing is often used to refine the retrieval results
to better satisfy users requirements. Previous methods
mainly focus on presenting users with relevant results.
However, in most cases, users cannot clearly present
their requirements by several query words. Therefore,
relevant results with rich topic coverage are more
likely to meet users’ ambiguous needs. In this paper, a
re-ranking method based on topic richness analysis is
proposed to enrich topic coverage in retrieval results.
Furthermore, a quantitative criterion called diversity
scores (DS) is proposed to evaluate the improvement.
Given a set of images, topics that are rarely included
in the set are scarce topics, as oppose to rich topics
that are widely distributed among the set. Scarce
topics contribute more than rich topics doto the DS of
images. Five researchers are invited to evaluate thereranked results both in topic coverage and relevance.
Experimental results on over 20,000 images
demonstrate that our proposed approach is effective in
improving the topic coverage of retrieval results
without loss of relevance.
2.5 Learning with Local and Global Consistency
We consider the general problem of learning from
labeled and unlabeleddata, which is often called semisupervised learning or transductive inference.A
principled approach to semi-supervised learning is to
design a classifying function which is sufficiently
smooth with respect to the intrinsic structure
collectively revealed by known labeled and unlabeled
points. We present a simple algorithm to obtain such a
smooth solution.Our method yields encouraging
experimental results on a number of classification
problems and demonstrates effective use of unlabeled
3.1 Content Based Image Retrieval:
CBIR requires less human interaction which tends to create
issues related to ability to deal with semantic attributes of
images. Computer systems are not able to accurately
extract all features of images alone [5]. In typical CBIR the
visual content of images are extracted and represented as
multidimensional feature vectors.
Fig 2:Search Results By Using CBIR
3.2 Text Based Image Retrieval:
The text-based image retrieval techniques use keywords,
the Content based image retrieval techniques use low-level
image features, the multimodal fusion techniques use a
combination of various image representative features, and
the semantic-based techniques use concepts. [4].
Fig 3:Image Search re-ranking using By TBIR
3.3 Visual Information Retrieval System:
During input, images are processed to compute the features
selected to represent the image contents. This process,
called indexation or indexing, assigns to each image a set
of identifying descriptors, or indices, which will be used by
the system in the matching phase to retrieve relevant
images and reject extraneous ones. The indices are stored
in the database, ideally are designed for efficient retrieval.
Different features (color, shape, texture, size, distance,
retrieval position, etc.) express different aspects of image
contents [6], only color based features are consider here
3.4 Tag Mining
In social media sharing services, such as the Flickr,
You tube, users are encouraged to share multimedia
data on the Web and annotate content with tags. Here
a tag is referred to as a descriptive keyword that
describes the multimedia content at semantic or
syntactic level [6].
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Special Issue – Active Galaxy
3.5 Tag ranking which aims to differentiate the tag
associated with the images with various levels of
3.6 Tag refinement with the propose to refine the
unreliable human-provided tags. Tag information
enrichment-which aims to supplement tags with
additional information.
Tagging of your pictures and images
Tagging of product images
Tagging of videos and video scenes
Electronic markings on image files
Tagging of other files such as PDFs or audio files
Categorization of your images and videos
Fig 4: Social Tagging Media Images
5. Iterative Algorithm
1) 1.function IterBSearch(A, x)
2) 2: s = 1
3) 3: f = length(A)
4) 4: while s _ f do
5) 5: m = (s + f)=2 . integer division
6) 6: if A[m] < x then
7) 7: s = m + 1
8) 8: else
9) 9: f = m 1
10) 10: end if
11) 11: end while
12) 12: p = s return p
13) 13: end function
Table1:Values Of Interest As The Function Of
Iteration Number
6.1 Boost Search Relevance For Tag-Based Social
Image Retrieval.
In this paper, Author proposes a relevance-based
ranking scheme for social image search, aiming to
automatically rank images according to their
relevance to the query tag. It integrates both the visual
consistency between images and the semantic
correlation between tags in a unified optimization
framework. Author suggest an iterative method to
solve the optimization problem, and the relevance
based ranking can thus be accomplished.
6.2 Social Image Search with Diverse Relevance
In this paper, Author suggest a social re-ranking
system for tag based image recovery with the thought
of image’s application and variety. We aim at reranking images according to their visual information,
semantic information and social clues. The initial
results include images contributed by different social
users. Usually each user contributes several images.
First we sort has images by inter-user re-ranking.
Users that have higher contribution to the given query
rank higher.
6.3 Towards relevant and diverse search of social
In This Paper, Author grants a request kind ranking
scheme which together takes relevance and diversity
into account by probing the content of images and
their connected tags. First, it estimates the relevance
scores of images with respect to the query term based
on both visual information of images and semantic
information of connected tags. Then indicative
comparisons of social images are evaluate based on
their tags.
6.4 Hierarchical clustering of WWW image search
results using visual, textual and link
In this paper, Author proposes a hierarchical
clustering method using visual, textual and link
analysis. By using a dream based page partition
algorithm, a web page is divided into blocks, and the
textual and link information of an image can be
accurately extracted from the block containing that
image. By using block-level link analysis techniques,
an image graph can be constructed. We then apply
spectral techniques to find a Euclidean embedding of
the images which respects the graph structure.
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International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
Special Issue – Active Galaxy
Architecture diagram shows the relationship between
different components of system. This diagram is very
important to understand the overall concept of system.
Architecture diagram is a diagram of a system, in
which the principal parts or functions are represented
by blocks connected by lines that show the
relationships of the blocks. They are heavily used in
the engineering world in hardware design, electronic
design, software design, and process flow diagrams
Fig5: Tag Graph Construction
Social re-ranking method for tag-based image
retrieval. In this social re-ranking method, inter-user
re-ranking and intra-user re-ranking are transported
out to obtain the recovered results. In order to enhance
the diversity performance, user information is firstly
introduced into our proposed method and obtains
acceptable results.
In this paper, we advocate a subject numerous reranking technique for tag-based photograph retrieval.
In this topic numerous re-ranking approach, internetwork rating and intra-network ranking are executed
to get pleasant retrieved outcomes. Tag graph
production and network detection are effective ways
to enhance the range. except, each tag’s word vector
is trained by way of the use of the Word2vec version
based totally at the English Wikipedia corpus to
enhance the relevance performance of the retrieved
1. Gaowen Liu, Jian Zhang, Kai Zheng,Sen Wang,
Yan Yan (2014),'Graph – Base Clustering And
Ranking For Diversified Image Search', proc.
Berlin,Heidelberg, DOl 10.1 007/s00530-0 140419-4.
2. Gao W, Huang T, Song K, Tian Y (2010),
'Diversifying the image retrieval results', in Proc.
ACM Int. Conf. Multimedia, Santa Barbara, CA,
USA, pp. 707-710.
3. X. Li, C. G. Snoek, and M. Worring, “Learning
social tag relevance by neighbor voting,” IEEE
Trans. Multimedia, vol. 11, no. 7, pp. 1310–1322,
Nov. 2009.
4. Y. Gao, M. Wang, H. Luan, J. Shen, S. Yan, and
D. Tao, “Tag-based social image search with
visual-text joint hypergraph learning,” in Proc.
ACM Conf. Multimedia, 2011, pp. 1517–1520.
5. L. Chen, D. Xu, W. Tsang, and J. Luo, “Tagbased web photo retrieval improved by batch
mode re-tagging,” in Proc. IEEE Int. Conf
Comput. Vis. Pattern Recognit., Jun. 2010, pp.
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