A novel approach to enable semantic and visual image summarization for exploratory image search
MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
LS-MMRM '09 Proceedings of the First ACM workshop on Large-scale multimedia retrieval and mining
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In this paper we propose a novel classification based framework for finding a small number of images summarizing a concept. Our method exploits metadata information available with the images to get the category information using Latent Dirichlet Allocation. We modify the import vector machine formulation based on kernel logistic regression to solve the underlying classification problem. We show that the import vectors provide a good summary satisfying important properties such as coverage, diversity and balance. Furthermore, the framework allows users to specify desired distributions over category, time etc, that a summary should satisfy. Experimental results show that the proposed method performs better than state-of-the-art summarization methods in terms of satisfying important visual and semantic properties.