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In this paper, we consider the image classification problem. Unlike conventional local learning technique, a novel framework, which is based on the proposed sparsity induced neighbors (SINs) instead of widely used k nearest neighbors, is presented. Within this framework, the SINs of test image are training images associated with the nonzero entries in the sparse representation of test image, and they...
We consider the image classification problem based on the similarities between images. The choice of the similarity is related to the particular applications, and it could be based on color, texture, bag-of-features, or even more complex kernels. As long as the pair-wise similarity matrix is transformed into a positive semidefinite one, the similarities of images could be treated as kernels. This...
We present a novel approach to measuring similarity between objects based on matching local “appearance contextual descriptor”. The descriptor has two components: Histogram of Oriented Gradient feature representing local patch appearance and the contextual descriptor capturing not only the spatial distribution of the non-reference patches relative to the reference patch but also the appearance similarities...
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