Zhengzheng Lou

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We present a novel approach to automatically discover object categories from a collection of unlabeled images. This is achieved by the Information Bottleneck method, which finds the optimal partitioning of the image collection by maximally preserving the relevant information with respect to the latent semantic residing in the image contents. In this method,(More)
We present a novel unsupervised data analysis method, Multi-feature Information Bottleneck (MfIB), which is an extension of the Information Bottleneck (IB). In comparison with the original IB, the proposed MfIB method can analyze the data simultaneously from multiple feature variables , which characterize the data from multiple cues. To verify the(More)
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