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By maximizing the gap between classes in the reproducing kernel Hilbert space (RKHS), our method optimizes for the sigma values of radial basis function (RBF) or gaussian kernels. For each sample, we try to ensure the distance gap between intra-class and inter-class in RKHS to be large. Unlike previous methods of multiple kernel learning, our method does not need large amount of computations, which...
This paper proposes an overall procedure of recognizing the machine reader zone of a real world picture of a passport. To begin with, the proposed method finds the area of passport from the input image and its rotation angle is determined. With the rectified passport image by counter-rotating the area of passport, the machine reader zone is found and an inverse projective transform is performed to...
Recently, there have been many proposals with state-of-the-art results in subspace clustering that take advantages of the low-rank or sparse optimization techniques. These methods are based on self-expressive models, which have well-defined theoretical aspects. They produce matrices with (approximately) block-diagonal structure, which is then applied to spectral clustering. However, there is no definitive...
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