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We propose a scheme for multi-layer representation of images. The problem is first treated from an information-theoretic viewpoint where we analyze the behavior of different sources of information under a multi-layer data compression framework and compare it with a single-stage (shallow) structure. We then consider the image data as the source of(More)
In this paper, we investigate the problem of visual information encoding and decoding for face recognition. We propose a decomposition representation with vector quantization and constrained likelihood projection. The optimal solution is considered from the point of view of the best achievable classification accuracy by minimizing the probability of error(More)
We analyze the privacy preservation capabilities of a previously introduced multi-stage image representation framework where blocks of images with similar statistics are decomposed into different codebooks (dictionaries). There it was shown that at very low rate regimes, the method is capable of compressing images that come from the same family with results(More)
In this work we address the problem of multi-class classification in machine learning. In particular, we consider the coding approach which converts a multi-class problem to several binary classification problems by mapping the binary labeled space into several partitioned binary labeled spaces through binary channel codes. By modeling this learning problem(More)
Compressive Sensing (CS) has become one of the standard methods in face recognition due to the success of the family of Sparse Representation based Classification (SRC) algorithms. However it has been shown that in some cases, the locality of the dictionary codewords is more essential than the sparsity. Also sparse coding does not guarantee to be local(More)