Real time google and live image search re-ranking
Refereed conference paper presented and published in conference proceedings


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AbstractNowadays, web-scale image search engines (e.g. Google Image Search, Microsoft Live Image Search) rely almost purely on surrounding text features. This leads to ambiguous and noisy results. We propose to use adaptive visual similarity to re-rank the textbased search results. A query image is first categorized into one of several predefined intention categories, and a specific similarity measure is used inside each category to combine image features for re-ranking based on the query image. Extensive experiments demonstrate that using this algorithm to filter output of Google Image Search and Microsoft Live Image Search is a practical and effective way to dramatically improve the user experience. A realtime image search engine is developed for on-line image search with re-ranking: http://mmlab.ie.cuhk.edu.hk/intentsearch. Copyright 2008 ACM.
All Author(s) ListCui J., Wen F., Tang X.
Name of Conference16th ACM International Conference on Multimedia, MM '08
Start Date of Conference26/10/2008
End Date of Conference31/10/2008
Place of ConferenceVancouver, BC
Country/Region of ConferenceCanada
Year2008
Month12
Day1
Pages729 - 732
ISBN9781605583037
LanguagesEnglish-United Kingdom
KeywordsAdaptive similarity, Image search, Intention, Visual

Last updated on 2021-08-01 at 01:24