International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 11, November 2013
ISSN 2319 - 4847
Region Based Image Retrieval Using MPEG-7
Visual Descriptor
Mr.V.A.Bingi1, Dr. R.C.Thool2
1
Walchand Institute of Technology, Solapur
Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded
2
Abstract
The popularity of digital images is promptly increasing due to advance technologies and innovations in digital Image acquisition
and storage technology. It has led to tremendous growth in large image database. Due to growing amount of multimedia data
accelerates the need of standards for multimedia content description efficient image retrieval technique. The initial step in
standardization was by establishing a standard for publishing multimedia data. MPEG-7 describes multimedia data like image,
audio and video [1]. Content based image retrieval is an image retrieval technique for retrieving semantically-relevant images
efficiently from an image database based on automatically-derived image features. The performance of content-based image
retrieval system is mainly limited by the gap between low-level features and high-level concepts of images [20]. To narrow down
this gap, region based image retrieval techniques are introduced.
The work focuses on use of visual descriptors to extract features of query image regions and creating clusters of image regions
based on visual description. Its main focus is on retrieving most similar images from large image database with visual
descriptors. Query image region feature are extracted by using visual color descriptors, texture descriptors. Each extracted
features of regions are compared with the specific cluster of image database based on their visual feature description. The most
similar images of each comparison is given to the user as an output.
Keywords:MPEG-7, Image retrieval , Region based image retrieval.
1. INTRODUCTION
Image retrieval system is concerned with searching and retrieving of digital images from collection of large digital image
database. The uses of text description to the images or visual features like color, shape, texture have been used for image
retrieval from database. Different types of image retrieval techniques are discussed below
 Text Based Image Retrieval
 Content Based Image Retrieval
 Region Based Image Retrieval
Text Based Image Retrieval
In early 1970s, text based image retrieval was widely used framework of image retrieval to annotate the images by text to
perform image retrieval. In TBIR, the images are manually annotated by text descriptors. Text descriptors are sometimes
inaccurate due to the subjectivity of human perception on complicated image feature very well. This technique requires
substantial level of human labor for manual annotation which is impractical for very large databases. To overcome all
these drawbacks content based image retrieval is introduced.
Figure 1: Text based Image Retrieval system.
Content Based Image Retrieval
Content based image retrieval [2] is an application of computer vision technique to address the problems allied with text
based image retrieval in large digital image database. In content based image retrieval the query is in the form of image
and its low level features are used as the content describing it. Low level features are set of characteristics of the image
such as color, texture, and shapes. These features are extracted from the query image as well as for all the images in the
database using feature extraction methods. Similarity measurement techniques are used to find out and retrieve the
similar images as shown in figure 2
Volume 2, Issue 11, November 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 11, November 2013
ISSN 2319 - 4847
.
Figure 2: Content based Image Retrieval system
Region Based Image Retrieval
Region based image retrieval is special type of CBIR which is more often used for image retrieval. It is well know that
the performance of content-based image retrieval system is mainly limited by the gap between low-level features and
high-level concepts of images. To narrow down this gap, region based image retrieval techniques are introduced. The
region based methods extract features of the segmented regions and perform similarity comparison at the granularity of
region. The main objective of using region features is to enhance the ability of capturing as well as representing the focus
of user’s perceptions of image content [3].
Figure 3: Region Based Image Retrieval system
2. RELATED WORK
NeTra [5] is image retrieval system based on color, texture and shape in segmented image regions. The UCSB Alexandria
Digital Library (ADL) developed this system. It uses an efficient automated image segmentation algorithm to represent
the image at object level.
Feng Jing, Megjing Li [3] proposed a framework that integrates efficient region based representation in terms of storage
and complexity. The framework consists of methods for region based image representation and indexing. In the proposed
framework, images in a database are first segmented into homogeneous regions and similar regions from all images are
clustered into smaller number of groups. They considered following issues while designing the image retrieval
framework.
i. Comparing two images by using image similarity measure.
ii. Making the system scalable.
iii. Improving retrieval accuracy progressively by interacting with the users.
In their proposed work, the image features are extracted only by using color moments. It didn’t consider other properties
like texture and shape. Image features are not extracted by using standards of MPEG-7 descriptors.
Sung Min Kim, Soo Jun Park and Chee Son Won introduced a query-by-layout framework which allows users to specify
edge and color layout in terms of sub-images. To this end, two MPEG-7 descriptors, namely, the edge histogram
descriptor (EHD) and the color layout descriptor (CLD) are employed. This is the first Query By Layout (QBL)-based
natural image retrieval system which is compliant with the MPEG-7 standard. The proposed work is focused on QBL they
have not considered the all regions of the image so ultimately it may reduces the accuracy of the system [6].
Volume 2, Issue 11, November 2013
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 11, November 2013
ISSN 2319 - 4847
3. PROPOSED WORK
The proposed system attempts to overcome the drawbacks of the CBIR by representing images at region level. In region
based image retrieval image has to be segmented into regions. Texture Boundary Encoding based (TBES) is used for the
automatic image segmentation [7]. It uses Dominant Color Descriptor (DCD) and Edge Histogram Descriptor (EHD) for
feature extraction of image regions. To reduce retrieval time, the image database will be clustered based on color and
texture. Self organizing map (SOM) algorithm will be used for clustering. The biggest advantage of using SOM is that it
can be easily applied to the large amount of data and it automatically clusters the input space and it not sensitive to
initialization [8].
Dominant Color Descriptor:
DCD of MPEG-7 provides efficient and compact color representation of an image or image region. DCD describes the
representative colors and level of each color in the image or region of image [9].
The DCD is defined as
f= {( Ci ,Pi, Vi), S}, i=1, 2...N
Where N is number of colors, Ci is the color index, Pi is percentage of color, Vi is color variance and S is the spatial
coherency.
Consider two color features f1={( Ci, Pi ,Vi),S} , i=1,2...N1 and f2={(Ci, Pi, Vi),S} , i=1,2...N2. In order two measure
similarities between two images is done by calculating following equation
D
Where a
i,
2
f1, f2  
N1
N2
i 1
j 1
N1
2
2
 P1i   P2 j  
N2

i 1 j 1
2a P P
1i , 2 1i 2 j
(1)
is the similarity coefficient between color clusters Ci
j
Edge Histogram Descriptor:
Using single feature for image retrieval may produce dissimilar results. The system may retrieve images not very similar
to query image or it will fail to retrieve images. To increase overall retrieval accuracy DCD will be combined with EHD.
EHD works efficiently for retrieval of images with non uniform textures [9].
Algorithm for Proposed System:
The algorithm for the proposed system is given below.
Step 1: Query image is given from the user.
Step 2: Perform TBES segmentation technique on the query image to get the regions.
Step 3: DCD & EHD features from regions are calculated and store them in an array known as feature vector.
Step 4: For every region, calculate the closest SOM node.
Step 5: Retrieve all the regions from the database that belong to closet SOM node.
Step 6: Compare the feature vectors of query image with the feature vectors of database images using Euclidean distance.
Step 7: Sort the distance in ascending order and Top 20 images are displayed on the screen.
Fig 4: Proposed System.
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International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 11, November 2013
ISSN 2319 - 4847
Region Matching:
Once the image is segmented into regions, features of region will be described by using feature vector of 20 normalized
attribute from f1 to f20. Feature vector from f1 to f16 are for texture and from f17 to f20 are for color. To measure
similarity between two images, we use the Euclidian distance between the feature vectors. The distance between two
regions Ri & Rj is defined as
16
20
k 1
k  17
dij  Wt  (fki  fkj)2  Wc  (fki  fkj)2
(2)
Where fki and fkj are the kth features of the regions Ri & Rj. Wt and Wc are the weights for texture and color features.
Appropriate values for Wt and Wc are 1 and 2 because we are considering 16 texture features and 4 color features.
5. EXPECTED RESULTS
Input – Query image
Output- The set images from image database which are similar to query image.
Precision and recall will be used to measure the performance of the system.
Precision=X/(X+Y)
Recall=X/(X+Z)
X -represents the number of relevant images that are retrieved
Y-Represents the number of irrelevant images
Z-number of relevant images which are not retrieved
The approximate performance of the system when user presents the query image with only single feature extraction that is
either color or texture is given below in the Table 1.
Table 1: Approximate Precision and Recall in Percentage by
Considering single feature at a time
Color
Texture
Precision
53.2
34.6
Recall
56.4
42.8
The performance of system using single feature may give inefficient result. Hence the proposed system uses multiple
features that color and texture to produce efficient results. The precision and recall values of the proposed system will be
certainly better than system using single feature. The approximate estimated performance of the proposed system is given
below in the Table 2.
Table 2: Precision and Recall values of
Proposed system Color and Texture at a time
Proposed Method
Precision
Above 75
Recall
Above 80
6. CONCLUSION AND FUTURE WORK
In this paper, we proposed a region based image retrieval system using a multi-featured combined approach of color and
texture information as attribute for similarity matching. Goal of our proposed work is “To use multiple visual feature of
image region for image retrieval in order to increase the performance of the retrieval system”. In order to further enhance
the performance of the system, shape feature can be used as third attribute which is the future work of the proposed
system.
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Volume 2, Issue 11, November 2013
Page 340
International Journal of Application or Innovation in Engineering & Management (IJAIEM)
Web Site: www.ijaiem.org Email: editor@ijaiem.org, editorijaiem@gmail.com
Volume 2, Issue 11, November 2013
ISSN 2319 - 4847
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