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In this paper, we propose a discriminative low-rank dictionary learning algorithm for sparse representation. Sparse representation seeks the sparsest coefficients to represent the test signal as linear combination of the bases in an over-complete dictionary. Motivated by low-rank matrix recovery and completion, assume that the data from the same pattern are linearly correlated, if we stack these data...
Histogram features, such as SIFT, HOG, LBP et al, are widely used in modern computer vision algorithms. According to [18], chi-square distance is an effective measure for comparing histogram features. In this paper, we propose a new method, named the Quadric-chi similarity metric learning (QCSML) for histogram features. The main contribution of this paper is that we propose a new metric learning method...
In this paper, we proposed a novel face representation method: Local Binary Pattern of Maximum Gabor Response. This method is inspired by the HMAX model in biological vision. We first use multi-scales & multi-orientations Gabor filters to simulate the response of simple cells in visual cortex. For a given scale, we get the maximum responses maps from all orientations. Then LBP operator is used...
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