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Impulse Technologies
                                        Beacons U to World of technology
        044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in
       IntentSearch: Capturing User Intention for One-Click Internet
                              Image Search
   Abstract
          1
            Web-scale image search engines (e.g., Google image search, Bing image search)
   mostly rely on surrounding text features. It is difficult for them to interpret users' search
   intention only by query keywords and this leads to ambiguous and noisy search results
   which are far from satisfactory. It is important to use visual information in order to solve
   the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet
   image search approach. It only requires the user to click on one query image with
   minimum effort and images from a pool retrieved by text-based search are reranked
   based on both visual and textual content. Our key contribution is to capture the users'
   search intention from this one-click query image in four steps. 1) The query image is
   categorized into one of the predefined adaptive weight categories which reflect users'
   search intention at a coarse level. Inside each category, a specific weight schema is used
   to combine visual features adaptive to this kind of image to better rerank the text-based
   search result. 2) Based on the visual content of the query image selected by the user and
   through image clustering, query keywords are expanded to capture user intention. 3)
   Expanded keywords are used to enlarge the image pool to contain more relevant images.
   4) Expanded keywords are also used to expand the query image to multiple positive
   visual examples from which new query specific visual and textual similarity metrics are
   learned to further improve content-based image reranking. All these steps are automatic,
   without extra effort from the user. This is critically important for any commercial web-
   based image search engine, where the user interface has to be extremely simple. Besides
   this key contribution, a set of visual features which are both effective and efficient in
   Internet image search are designed. Experimental evaluation shows that our approach
   significantly improves the precision of top-ranked images and also the user experi- nce.




   1


  Your Own Ideas or Any project from any company can be Implemented
at Better price (All Projects can be done in Java or DotNet whichever the student wants)
                                                                                               1

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18

  • 1. Impulse Technologies Beacons U to World of technology 044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in IntentSearch: Capturing User Intention for One-Click Internet Image Search Abstract 1 Web-scale image search engines (e.g., Google image search, Bing image search) mostly rely on surrounding text features. It is difficult for them to interpret users' search intention only by query keywords and this leads to ambiguous and noisy search results which are far from satisfactory. It is important to use visual information in order to solve the ambiguity in text-based image retrieval. In this paper, we propose a novel Internet image search approach. It only requires the user to click on one query image with minimum effort and images from a pool retrieved by text-based search are reranked based on both visual and textual content. Our key contribution is to capture the users' search intention from this one-click query image in four steps. 1) The query image is categorized into one of the predefined adaptive weight categories which reflect users' search intention at a coarse level. Inside each category, a specific weight schema is used to combine visual features adaptive to this kind of image to better rerank the text-based search result. 2) Based on the visual content of the query image selected by the user and through image clustering, query keywords are expanded to capture user intention. 3) Expanded keywords are used to enlarge the image pool to contain more relevant images. 4) Expanded keywords are also used to expand the query image to multiple positive visual examples from which new query specific visual and textual similarity metrics are learned to further improve content-based image reranking. All these steps are automatic, without extra effort from the user. This is critically important for any commercial web- based image search engine, where the user interface has to be extremely simple. Besides this key contribution, a set of visual features which are both effective and efficient in Internet image search are designed. Experimental evaluation shows that our approach significantly improves the precision of top-ranked images and also the user experi- nce. 1 Your Own Ideas or Any project from any company can be Implemented at Better price (All Projects can be done in Java or DotNet whichever the student wants) 1