document: A classification based framework for concept summarization 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 .
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