NAVIGATION-PATTERN-BASED RELEVANCE FEEDBACK ......Satyanarayana Mummana 1, Swathi Koundinya 2 1 Asst...
Transcript of NAVIGATION-PATTERN-BASED RELEVANCE FEEDBACK ......Satyanarayana Mummana 1, Swathi Koundinya 2 1 Asst...
SATYANARAYANA MUMMANA* et al. ISSN: 2250–3676
[IJESAT] INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE & ADVANCED TECHNOLOGY Volume-2, Issue-5, 1241 – 1246
IJESAT | Sep-Oct 2012
Available online @ http://www.ijesat.org 1241
NAVIGATION-PATTERN-BASED RELEVANCE FEEDBACK FOR CONTENT
BASED IMAGE RETRIEVAL
Satyanarayana Mummana 1
, Swathi Koundinya 2
1
Asst Professor, Dept of Computer Science and Engineering, Avanthi Institute of Engineering & Technology,
Visakhapatnam, Andhra Pradesh.
2 Final M.Tech Student,
Dept of Computer Science and Engineering, Avanthi Institute of Engineering & Technology,
Visakhapatnam, Andhra Pradesh.
Abstract The current day scenario demands drastic enhancement in information search engines. The quest for information or representation of
information is more compact and precise to point when represented as image than a near text. As a result image search has gained an
intense popularity. Searching or retrieving image based on its content is called content based image retrieval. There are various
synch methods so far implemented and all of these methods are also support by feedback system from the user to fine tune the search
results. But still because of intelligence modules implemented and time factors those methods are impractical away for real
applications. Thus this work proposes a new user navigation pattern based feedback system to support content based image retrieval.
Index Terms: Navigation, Image Retrieval,
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1. INTRODUCTION
The growing multimedia quest has raised the interest of
mining multimedia content recently. The image retrieval from
large multimedia repository is a difficult but interesting task.
The searches use the image annotation captions for text based
search quest and thus content base image retrieval gains access
to the requisition. These suffer from two major drawback large
caption annotation required and captions are expected to be as
relevant as possible to the image description.
As a result, a number of powerful image retrieval algorithms
have been proposed to deal with such problems over the past
few years. Content-Based mage Retrieval (CBIR) is the
mainstay of current image retrieval systems. In general, the
purpose of CBIR is to present an image conceptually, with a
set of low-level visual features such as color, texture, and
shape. That is, existing methods refine the query again and
again by analyzing the specific relevant images picked up by
the users. Especially for the compound and complex images,
the users might go through a long series of feedbacks to obtain
the desired images using current RF approaches.
Fig. 1.Motivating example for the problem of exploration
convergence
The involved problem, so-called visual diversity, is shown in
Fig. 2. In this case, if the compound concept to aim at consists
of “car,” “sunset,” and “sunset and car,” it is not easy for
traditional CBIR methods to capture the user’s intention.
Especially for query point movement methods, this problem
will result in that the features would converge toward the
specific point in the feature space during the query session.
Hence, it is still hard to cover the concepts of “car,” “sunset,”
and “sunset and car” even by performing the weighted K-
Nearest Neighbors (KNNs) search.
SATYANARAYANA MUMMANA* et al. ISSN: 2250–3676
[IJESAT] INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE & ADVANCED TECHNOLOGY Volume-2, Issue-5, 1241 – 1246
IJESAT | Sep-Oct 2012
Available online @ http://www.ijesat.org 1242
The rest of this work is so organized that the Section 2 briefly
shares the related contributions so far occurred in this field
and the Section 3 exposes our proposed method and that
followed by the conclusion.
Fig. 2. Example of visual diversity
2. RELATED CONTRIBUTIONS
Some previous work keeps an eye on investigating what visual
features are important for those images (positive examples)
picked up by the users at each feedback (also called iteration
in this paper).
Fig. 3. Relevance feedback with generalized QR technique
In this work, the feature weights are dynamically updated to
connect low-level visual features and high-level human
concepts. NNEW, developed by You et al. [24], learns the
user’s query from positive and negative examples by
weighting the important features. For this kind of approach, no
matter how the weighted or generalized distance function is
adapted, the diverse visual features extremely limit the effort
of image retrieval. Fig. 4 illustrates this limitation that
although the search area is continuously updated by
reweighting the features, some targets could be lost.
Then the user can obtain a set of most relevant web images
according to the metadata or the browsing log. However, if the
result does not satisfy the user, the query refinement can be
easily incorporated into the query procedure
In fact, usage mining has been made on how to generate users’
browsing patterns to facilitate the web pages retrieval.
Similarly, for web image retrieval, the user has to submit a
query term to the search engine, so-called textual-based image
search.
This is why CBIR using RF has been the focus of the
researchers in the field of image retrieval. As far as the usage
log of CBIR is concerned, the challenge mainly lies on: how
to generate and utilize the discovered patterns. In this paper,
we develop a navigation-pattern based data structure
permeated by the query point movement aspect, which has
never been proposed by past studies. Through the special data
structure, the user’s intention can be caught more quickly and
precisely.
3. THE NAVIGATION PATTERN BASED
RELEVANCE FEEDBACK
This section is designed to state focus on the proposed
algorithm. This section evolves the problem statement and
formulates the algorithm application.
Fig.4. Example of navigation pattern trees.
Indeed, these unsolved problems result in large limitation in
RF. Perhaps, the aged hybrid systems fusing the results
generated by multiple query refinement systems can look for
the better results than individual systems. After eliminating
the redundant patterns, the trimmed navigation pattern tree
reduces the search cost significantly. Based on the navigation
pattern tree, the desired images can be captured more
promptly without repeating the scan of the whole image
database at each feedback, especially for the large-scale image
data. Nevertheless, the expensive computation cost makes it
impractical in real applications.
SATYANARAYANA MUMMANA* et al. ISSN: 2250–3676
[IJESAT] INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE & ADVANCED TECHNOLOGY Volume-2, Issue-5, 1241 – 1246
IJESAT | Sep-Oct 2012
Available online @ http://www.ijesat.org 1243
The basic idea of this operation is to find the images not only
with the specific similarity function. By recursively modifying
the query point, the search direction can move toward the
targets gradually. Assume that a set of images is found by the
query point qpold at the preceding feedback.
By performing a weighted KNN search, QEX-like procedure
first determines the nearest query seed to each of G, called
positive query seed, and the nearest query seed to each of N,
called negative query seed.
Additionally, the slight loss of the information embedded in
the negative examples is also deliberated in this paper. In
theory, if the negative query seeds are all dropped at each
feedback, the desired results could be captured more precisely.
However, there exist some query seeds belonging to both of
the positive query seed set and the negative query seed set at
each feedback. Dropping the negative query seeds would lead
to the loss of positive query seeds. Lines 5-8 of Fig. 10 show
how to find the positive and negative query seed sets. As a
result, a set of positive query seeds is selected to be the start of
potential search paths.
SATYANARAYANA MUMMANA* et al. ISSN: 2250–3676
[IJESAT] INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE & ADVANCED TECHNOLOGY Volume-2, Issue-5, 1241 – 1246
IJESAT | Sep-Oct 2012
Available online @ http://www.ijesat.org 1244
That is, these dropped negative seeds may be the start of good
search paths. To take account of both positive and negative
information simultaneously, every seed has its own token
rth.chk. If the seed owns the maximum number of negative
examples or owns no positive example, it will be tokenized as
a bad manner, i.e., rth:chk ¼ 0, as shown in lines 4 and 15 of
above algorithm. Otherwise, rth.chk is 1 for any good manner.
4. RESULTS AND CONCLUSION
Before discussing the experimental results the below is the
sample representation of user navigation patterns.
The dataset consumed for the experimentation is as below
Various simulation regression efforts of this proposed system
as been tested with various bench marks functions and
databases and are graphitized below.
Fig. 6. The average precisions of different s for data set 3.
In most former approaches, an important limitation for image
retrieval is that the explosive growth of images leads to poor
and unstable performance. This further proves that our
approach is very robust in the success of RF for the large-scale
image data
Fig. 7. The precisions of different approaches for data set 7.
Finally to precise, the main feature of NPRF is to efficiently
optimize the retrieval quality of interactive CBIR. On one
hand, the navigation patterns derived from the users’ long
term browsing behaviors are used as a good support for. First,
in view of very large data sets, we will scale our proposed
method by utilizing parallel and distributed computing
techniques
SATYANARAYANA MUMMANA* et al. ISSN: 2250–3676
[IJESAT] INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE & ADVANCED TECHNOLOGY Volume-2, Issue-5, 1241 – 1246
IJESAT | Sep-Oct 2012
Available online @ http://www.ijesat.org 1245
The experimental results reveal that the proposed approach
NPRF is very effective in terms of precision and coverage.
Within a very short term of relevance feedback, the navigation
patterns can assist the users in obtaining the global optimal
results. Moreover, the new search algorithm NPRFSearch can
bring out more accurate results than other well-known
approaches.
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BIOGRAPHIES
Satyanarayana Mummana is working as
an Asst. Professor in Avanthi Institute of
Engineering & Technology,
Visakhapatnam, Andhra Pradesh. He has
received his Masters degree (MCA) from
Gandhi Institute of Technology and
Management (GITAM), Visakhapatnam
and M.Tech (CSE) from Avanthi Institute
of Engineering & Technology, Visakhapatnam. Andhra
Pradesh. His research areas include Image Processing,
Computer Networks, Data Mining, Distributed Systems,
Cloud Computing.
Swathi Koundinya Completed her BTech
and pursuing MTech in from Avanthi
Institute of Engineering & Technology,
Visakhapatnam. Andhra Pradesh Interesting
areas are data mining and .net technologies
and MySQL database