Abstract
Image search reranking has received great attention since it overcomes the drawback of “only textual features utilization” in nowadays web-scale image search engines. Most of existing methods focus on relevance reranking, that is reordering the returned results according to their relevance with the query. However, in many cases, users cannot precisely and exhaustively describe their requirements by several query words. Therefore, relevant results with more diversity are more easily meet the users’ ambiguous purpose. To address this problem, in this paper, we proposed a DIR (DDrank-based Image search Rerank) algorithm, which can enrich the topic coverage while keeping the relevance with minimal impact. DIR is based on vertex reinforced random walk and further curbing neighbor items’ growing rate. Therefore, DIR can automatically balance the relevance and diversity of the top ranked vertices in a principle way. Extensive experiments are performed in a popular image dataset, and the results demonstrate the superiority against other existing methods in the criterion of both AP and ADP.
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Ji, Z., Li, J., Su, Y., He, Y., Pang, Y. (2013). Balance between Diversity and Relevance for Image Search Results. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_67
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DOI: https://doi.org/10.1007/978-3-642-36669-7_67
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