2012年
Re-ranking by multi-modal relevance feedback for content-based social image retrieval
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
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- 巻
- 7235
- 号
- 開始ページ
- 399
- 終了ページ
- 410
- 記述言語
- 英語
- 掲載種別
- DOI
- 10.1007/978-3-642-29253-8_34
With the recent rapid growth of social image hosting websites, it is becoming increasingly easy to construct a large database of tagged images. In this paper, we investigate whether and how social tags can be used for improving content-based image search results, which has not been well investigated in existing work. We propose a multi-modal relevance feedback scheme and a supervised re-ranking approach by using social tags. Our multi-modal scheme utilizes both image and social tag relevance feedback instances. The approach propagates visual and textual information and multi-modal relevance feedback information on an image-tag relationship graph with a mutual reinforcement process. We conduct experiments showing that our approach can successfully use social tags in the re-ranking of content-based social image search results and perform better than other approaches. Additional experiment shows that our multi-modal relevance feedback scheme significantly improves performance compared with the traditional single-modal scheme. © 2012 Springer-Verlag Berlin Heidelberg.
- ID情報
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- DOI : 10.1007/978-3-642-29253-8_34
- ISSN : 0302-9743
- ISSN : 1611-3349
- SCOPUS ID : 84859705588