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Recently, the sparse representation-based classification (SRC) methods have been successfully used for the classification of hyperspectral imagery, which relies on the underlying assumption that a hyperspectral pixel can be sparsely represented by a linear combination of a few training samples among the whole training dictionary. However, the SRC-based methods ignore the sparse representation residuals...
In this paper, we present a modified self-training semi-supervised SVM algorithm. In order to demonstate its validity and effectiveness, we carry out some experimentswhich prove that our method is better than the former algorithm. Using our modified self-training semi-supervised SVM algorithm, we can save much time for lableling the unlabelled data.
In the work, an example-based method of touching string segmentation is proposed. Using Markov random field, the candidate patches based on the compatibility of the neighbour patches are selected. The outputs of the MRF after the iterative belief propagation form a segmentation probability map. The cut position is extracted from the map. Experiment results are presented and demonstrate the effectiveness...
Linear discrimination analysis (LDA) is one of the most popular feature extraction and classifier design techniques. It maximizes the Fisher-ratio between between-class scatter matrix and within-class scatter matrix under a linear transformation, and the transformation is composed of the generalized eigenvectors of them. However, Fisher criterion itself can not decide the optimum norm of transformation...
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