Optimizing content based image retrieval in p2 p systems

IJRET: International Journal of Research in Engineering and Technology eISSN: 2319-1163 | pISSN: 2321-7308
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Volume: 03 Issue: 05 | May-2014, Available @ http://www.ijret.org 107
OPTIMIZING CONTENT BASED IMAGE RETRIEVAL IN P2P SYSTEMS
Swathi H1
, Sandeep Kumar2
, Suresh L3
1
Student, Computer Science Dept, , Cambridge Institute of technology, Bangalore, India
2
Associate Professor, Head, Information Science Dept, Cambridge Institute of technology, Bangalore, India
3
Principal, Cambridge Institute of technology, Bangalore, India
Abstract
Content Based image retrieval (CBIR) is next big thing on search market. Performing content based image retrieval on internet
databases connected using P2P network is the scope of our work. In case of unstructured P2P network CBIR has many challenges in
terms of routing, match making etc. Authors in [1] have proposed a P2P-CBIR search engine to provide scalable image retrieval
which can adaptively control the query scope and progressively define the accuracy of query results. But the problem in this solution
is that query search time is high. We take this problem and propose optimizations to work in [1], so that the image search time can be
reduced.
Keywords: peer-to-peer networks; Multi-instance query; content based image retrieval; scalable retrieval; network
search configuration.
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1. INTRODUCTION
Advancement of digital media coding and Internet technology
have enabled, Peer-to-Peer (P2P) networks to share files,
transferring real-time video streams, and performing Content-
Based Image Retrieval (CBIR) in recent decades. In the P2P
networks, each connected peer serves simultaneously as a
server and a client, which can distribute computation and
network traffics to peers to provide efficient streaming and
CBIR services.
The CBIR has been developed over the past decade since
international image/video coding standards, such as JPEG,
MPEG-4, H.264, and HEVC, have been widely used and
distributed over Internet. The CBIR engine can help to find user
interested relevant multimedia contents, either through multi-
instance query or relevance feedback control. Before the CBIR
search engine being developed, text information is the only
precise data used to perform content similarity retrieval, such as
filename, creator, and content descriptions.
However, the text-based CBIR requires human annotation and
content categorization, such that large scale retrieval is not
feasible. In addition, the categorization and annotations would
be different through different human labeling, which would bias
the retrieval results.
To perform server-client CBIR, the server has to record
addresses and feature characteristics of all client peers. To
respond a query, the server helps the query peer, P, to forward
the query message, Q, to all peers with relevant contents, which
would perform retrieval and transmit relevant images toward P.
This centralized approach suffers heavy network traffics in that
unnecessary retrieval and transmission are involved. To
eliminate the centralized traffic loading, P2P CBIR is proposed.
Multi-instance query is widely used by the CBIR search engine
to improve retrieval accuracy and reduce retrieval processing
time. As one image would demonstrate various types of
features, performing multi-instance query with multiple feature
(MIMF) types can yield more accurate and robust retrieval
results, which can be achieved by assigning weighting to
different similarity ranks corresponding to different feature
types. Authors in [1] proposed to perform MIMF on the P2P
paradigm for progressive and scalable retrieval. In this method,
the query search time was high because of the network delay
and image matching at each peer. We take this problem as our
motivation and propose methods to optimize the query search
time.
2. RELATED WORK
The MPEG-7 standard provides normative descriptors, such as
color, texture, region, and shape descriptors, for effective visual
data retrieval [2]. These descriptors represent visual contents
with numerical feature values from which the similarity could
be measured quantitatively. They provide a numerical
measurement space for image knowledge discovery and data
mining (KDD). In general, one CBIR search engine provides
the relevance feedback function to recognize user’s definition
on image similarity. In addition, utilizing multiple feature types
would help the search engine to increase the retrieval accuracy
and reduce the network traffic in the P2PCBIR system.
Previous works, such as IBM’s Query by Image Content
(QBIC) [3], Blobworld [4], and visualSEEk [5], proposed to

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extract low-level features from locally segmented image
regions. For the QBIC system, the objects or regions in images
are segmented and labeled manually. In terms of feature
description, the color, texture, and shape features could be
extracted from the objects, regions, or the whole image. The
Blobworld proposed to segment an image into a set of region
that are coherent in color and texture features. The similarity
between the query image and the one in the image database is
computed through the similarity regions matching. The
VisualSEEk extracts the color and texture features from the
salient regions segmented in the images, and further takes the
geometric properties of regions (e.g., size, absolute, and relative
spatial locations) into considerations.
PicToSeek [6] is an object based retrieval system, in which the
invariants of color and shape features for the object in each
image are evaluated based on the criteria: illumination
conditions, viewpoint invariance, and geometry properties of
the object.
However, how to justify segmentation results become critical in
terms of image segmentation techniques. In addition, the
problem of computing a similar region pair set with the
maximum covered area is NP-hard [7].
Several researches were conducted to study the multi-instance
learning measure to select the semantic regions from query
images automatically [8][9]. The selected regions from query
images are labeled as common positive from users’ relevance
feedback.
Some researches proposed to extract global or local features
from images to perform image classification and image/video
retrieval [13]-[16]. The Scale Invariant Feature Transform
(SIFT) [17][18] is a widely used local feature descriptor that
extracts distinctive invariant key points from local patches in
images. According to the clustering procedure, each patch
described by the feature key point is categorized into the set
corresponding to specific words. Therefore, a bag of-words
(BOW) [19][20] is built as the feature vector to describe the
visual content of one image.
Speeded Up Robust Features (SURF) [21] descriptor
approximates the second order Gaussian derivatives in Hessian
matrix, and exploits integral images to extract the local feature
points. Principal Component Analysis (PCA)-SIFT [22]
projects the normalized gradient image vector to a compact
feature vector. Since SURF and PCA-SIFT descriptors
demonstrate lower dimensional feature vectors [23], they spend
less time for matching between two images, as compared to the
SIFT descriptor. In contrast, global features can be extracted
from images such as color correlogram and Tamura texture
descriptors. The color correlogram [24] presents the spatial
correlation between pairs of pixels’ colors with distance. As
compared to the color histogram, color correlogram is more
robust to different image processing procedures, including
spatial translation, different view of the same scene, and large
changes in appearance. Tamura texture descriptors [25] extract
six basic texture features corresponding to human visual
perception: coarseness, contrast, directionality, line-likeness,
regularity, and roughness.
Experiments suggest that the first three texture features, i.e.,
coarseness, contrast, and directionality, are efficiently in images
classification and segmentation.
3. PROBLEM STATEMENT
The P2P-CBIR system diagram proposed in [1] is demonstrated
in Fig. 1, which comprises off- and on-line stages.
Fig. 1: The P2P-CBIR system diagram.
At the off-line stage, an image pre-processing module was
integrated in each peer to extract descriptors of new images,
such as color, texture, and shape. At the on-line stage, the query
peer P that received the Q performs MIMF.
The problem in this approach is that the query feature is
matched against each image in the peer during the online search
stage. This results in bigger search time.
4. PROPOSED SOLUTION
We introduce two additional concepts in offline phase of P2P-
CBIR which will help to achieve reduced search time.
1. Clustering of Peers
2. Constructing search index for Peer cluster
In the online phase, the way of searching is modified to achieve
fast search time.

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4.1 Clustering of Peers
Peers must be grouped together based on any parameters like
peers in a particular geographical area. Each peer will advertise
the number of requests it has received till recent to all the
neighbors in its geographical area with a distance of K hops.
Once all the peers have exchanged the information, the peer
node with maximum number of request till now will become
the cluster head. All the other peers will come the part of the
cluster. A peer node can also be part of two clusters. This
occurs for the case of boarder peers. The cluster head peer will
start a mobile agent. This mobile agent will visit all the peers in
the cluster and visit the cluster head. The mobile agent will
carry information of the image feature cluster information in
each peer to the cluster head peer.
4.2 Constructing Search Index for Peer Cluster
The mobile agent will bring the feature vector of images in each
peer to the cluster head peer. Based in this clustering is again
done to group the image features. The mobile agent cannot
bring the all image feature information from other peers at one
shot. So the clustering algorithm which we will implement must
be a agglomerative clustering algorithm. With this cluster
information, a search index must be constructed. The search
index will maintain the map of what features are available at the
peer cluster as the whole and the whereabouts of each features
in the peer cluster.
4.3 Modified Search
In the search phase, when the query is sent over P2P network,
each peer will forward the query to its cluster head peer. Once
the cluster head peer receives the query, it will search in the
search index and look for the presence of the target feature
presence or similarity with any features in its search index. If
any matching entry is found the query is redirected to the peers
in which feature is found. If the no matching entry is found, the
cluster head will forward to neighbor cluster head.
5. ADVANTAGES OF OUR SOLUTION
With the search index constructed and kept at peer cluster head
node, the search on each peer for the match is avoided. This
way the search time is shortened. With the bigger cluster size,
the search time can be reduced drastically.
6. PERFORMANCE ANALYSIS
The databases comprise 500 images, some of which are
collected from Corel image database and Caltech 101, and the
others are collected from professional websites. Each peer is
distributed 15 images randomly different categories. For peer
clustering, each peer records three random links and two
attractive links for CH, SCD, CSD, CLD, EHD and DWT
features, respectively.
We measured two parameters accuracy of match and the search
time. Our main objective in this paper work is to reduce the
search time without affecting the accuracy in a bigger way.
We compared our proposed solution to the solution proposed in
[1].
From the performance result, we see that the search time is less
than the P2P-cbir system proposed in [1]. The reason for this
reduction is because of the peer search index maintained in the
cluster head. Due to this, we have shortened the search area and
it reflects in reduction in search time.
The accuracy is comparatively lesser in our approach during
initial stages, but improves with number of nodes because of
more results returned to the user.
7. CONCLUSIONS AND FUTURE WORK
We have detailed our proposed solution for reducing the search
time for CBIR in P2P-CBIR system. In future we plan to
analyze the performance of our solution in terms of search time
for different cluster sizes and compare with existing solution. In
addition we also plan to propose a representation of search
index to enable faster search using spare coding representations.

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REFERENCES
[1]. Chun-Rong Su, Jiann-Jone Chen, and Kai-Lin Chang
“Content-Based Image Retrieval on Reconfigurable Peer-to-
Peer Networks” 2013 International Symposium on Biometrics
and Security Technologies.
[2]. Overview of the MPEG-7 Standard (version 10). ISO/IEC
JTC1/SC29/WG11 N6828, Oct. 2004.
[3]. M. Flickner, et al., “Query by image and video content: the
QBIC system,” IEEE Computer, vol. 28, no. 9, pp. 23-32, 1995.
[4]. C. Carson, et al, “Blobworld: image segmentation using
expectation maximizations and it application to image
querying,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 24, pp.
1026V38, 2002.
[5]. J. R. Smith and@S. F. Chang, “Visualseek: a fully
automated contentbased image query system,” in Proc. ACM
Multimedia, 1996.
[6]. T. Gevers and A. Smeulders, “Pictoseek: combining color
and shape invariant features for image retrieval,” IEEE Trans.
Image Process, pp. 102-119, 2000.
[7]. P. Natsev, et al., “WALRUS: a similarity matching
algorithm for image databases,” Technical report, Bell
Laboratories, Murray Hill, 1998.
[8]. R. Rahmani, et al., “Localized content based image
retrieval,” IEEE Trans. Pattern Anal. Mach. Intell., 2008.
[9]. Tram Truong Huu& John Montagnat: Virtual Resource
Allocations distribution on a cloud infrastructure (IEEE, 2010),
pp.612-617.
[10]. Y. Chen, J. Bi, and J. Z. Wang, “MILES: multiple-
instance learning via embedded instance selection,” IEEE
Trans. Pattern Anal. Mach. Intell., vol. 28, pp. 1931-1947, Dec.
2006.

Optimizing content based image retrieval in p2 p systems

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