In the last few years, we have seen an upsurge of interest in content-based image retrieval (CBIR) the selection of images from a collection via features extracted from images themselves. Typically the nearcst-neighbor rule is used to retrieve images from a query image. However, the underlying query distribution may not be isotropic in nature. Hence, a more sophisticated estimation for the query distribution is required. We propose a novel relevance feedback framework for image retrieval which contains two stages: (1) to estimate the query distribution based on relevance feedback information and (2) to generate a set of inquiries for relevance selection based on the maximum Entropy principle. We demonstrate these two stages in detail. Moreover, experiments have been performed on a trademark image database. The results show our proposed framework is effective in image retrieval with a few relevant samples.
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