Abstract
Though current commercial image search engines provide effective ways to retrieve the relevant images, they are ineffective for users to find the desired from the retrieved hundreds of results, especially for ambiguous queries. In this paper, we propose to summarize the search results by several representative images. We argue that the relevance and image quality are two important measures for a user friendly summarization since image search results are normally noisy with some low-quality images. The two factors, which can be regarded as informative prior of whether an image is a good summary candidate, are modeled into Affinity Propagation framework. User studies demonstrate that our proposed method is able to produce a user friendly summary, in terms of relevance, diversity, and coverage.
This work was performed when Rui Liu was visiting Microsoft Research Asia as a research intern.
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Liu, R., Yang, L., Hua, XS. (2010). Image Search Result Summarization with Informative Priors. In: Zha, H., Taniguchi, Ri., Maybank, S. (eds) Computer Vision – ACCV 2009. ACCV 2009. Lecture Notes in Computer Science, vol 5996. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12297-2_47
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DOI: https://doi.org/10.1007/978-3-642-12297-2_47
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