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.
CITATION STYLE
Li, J., Ma, Q., Asano, Y., & Yoshikawa, M. (2012). Re-ranking by multi-modal relevance feedback for content-based social image retrieval. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7235 LNCS, pp. 399–410). https://doi.org/10.1007/978-3-642-29253-8_34
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