With the recent rapid growth of social image hosting websites, such as Flickr, it is easier to construct a large database with tagged images. Social tags have been proven to be effective for providing keyword-based image retrieval and widely used on these websites, but whether they are beneficial for improving content-based image retrieval has not been well investigated in previous work. In this paper, we investigate whether and how social tags can be used for improving content-based image search results. We propose an unsupervised approach for automatic ranking without user interactions. It propagates visual and textual information on an image-tag relationship graph with a mutual reinforcement process. We conduct experiments showing that our approach can successfully use social tags for ranking and improving content-based social image search results, and performs better than other approaches. © 2011 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Li, J., Ma, Q., Asano, Y., & Yoshikawa, M. (2011). Ranking content-based social images search results with social tags. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7097 LNCS, pp. 147–156). https://doi.org/10.1007/978-3-642-25631-8_14
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