A UNIFIED RELEVANCE FEEDBACK FRAMEWORK FOR WEB IMAGE

convertingtownSoftware and s/w Development

Nov 4, 2013 (3 years and 8 months ago)

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1

A UNIFIED RELEVANCE FEEDBACK FRAMEWORK FOR WEB IMAGE
RETRIEVAL



INTRODUCTION



Although relevance feedback (RF) has been extensively studied in the
content
-
based image retrieval community, no commercial Web image search engines support RF
because of scalability, efficiency, and effectiveness issues. In this paper, We propose a unifie
d
relevance feedback framework for Web image retrieval. Our framework shows advantage over
traditional RF mechanisms in the following three aspects. First, during the RF process, both
textual feature and visual feature are used in a sequential way.



To seamlessly combine textual feature
-
based RF and visual feature
-
based
RF, a query concept
-
dependent fusion strategy is automatically learned. Second, the textual
feature
-
based RF mechanism employs an effective search result

clustering (SRC) algorithm to
obtain salient phrases, based on which we could construct an accurate and low
-
dimensional
textual space for the resulting Web images. Thus, we could integrate RF into Web image
retrieval in a practical way. Last, a new user i
nterface (UI) is proposed to support implicit RF.
On the one hand, unlike traditional RF UI which enforces users to make explicit judgment on the
results, the new UI regards the users’ click
-
through data as implicit relevance feedback in order
to release b
urden from the users. On the other hand, unlike traditional RF UI which hardily
substitutes subsequent results for previous ones, a recommendation scheme is used to help the
users better understand the feedback process and to mitigate the possible waiting
caused by RF.
Experimental results on a database consisting of nearly three million Web images show that the
proposed framework is wieldy, scalable, and effective.






Head office: 2
nd

floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad

www.kresttechnology.com
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ail:

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,

Ph: 9885112363 / 040 44433434


2

W
ith the explosive growth of bo
th World Wide Web and the number of digital images,
there is more and more urgent need for effective Web image retrieval systems. Most of the
popular commercial search engines, such as Google, Yahoo and AltaVista, support image
retrieval by keywords. There

are also commercial search engines dedicated to image retrieval,
e.g., Pic search. A common limitation of most of the existing Web image retrieval systems is that
their search process is passive, i.e., disregarding the informative interactions between use
rs and
retrieval systems. An active system should get the user into the loop so that personalized results
could be provided for the specific user. To be active, the system could take advantage of
relevance feedback techniques.

METHODOLOGY


NEED FOR RELEVAN
T FEEDBACK TECHNIQUES


The main
criterion of the relevant feedback is

whenever

the thing is
passive (which does not respond to the given system). It provides the activeness (which will
responds to the given system). Common limitation for the most of the search engines are their
search process is passive in order to avoid this drawba
ck we are going for the relevance feedback
which makes the system active.

USE OF UNIFIED RELEVANT FEEDBACK FRAME WORK


It is meaningful for the web image retrieval which combines both
textual based RF and the visual based RF in a

sequential way.

In this paper, we have presented a
unified relevance feedback framework for Web image

retrieval. During RF process, both textual
features and visual features are used in a sequential way. A dynamic multimodal fusion strategy
is proposed to seamlessly combine the RF in textual space and that in visual space.


Head office: 2
nd

floor, Solitaire plaza, beside Image Hospital, Ameerpet, Hyderabad

www.kresttechnology.com
, E
-
M
ail:

krestinfo@gmail.com

,

Ph: 9885112363 / 040 44433434


3


To integrate RF into Web image
retrieval in a practical way, the textual feature
-
based RF
mechanism employs an effective search result clustering (SRC) algorithm to construct an
accurate and low
-
dimensional textual space for the resulting Web images. Besides explicit
relevance feedback,

implicit relevance feedback, e.g., click
-
through data, can also be integrated
into the proposed mechanism. Then, a new user interface (UI) is proposed to support implicit RF.
Experimental results on a database consisting of nearly three million Web images

show that the
proposed mechanism is wieldy, scalable, and effective.

References

1. Google Image Search
, [Online]. Available: http://images.google.com

2.
Yahoo Image Search
, [Online]. Available: http://images.search.yahoo.com/

3.
Al
tavisa Image Search
, [Online]. Available: http://www.altavista.com/ image/

4.
Picsearch Image Search
, [Online]. Available: http://www.picsearch.com