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The combination of spatial and spectral information of hyperspectral image benefits the improvement of classification accuracy. The structured sparse coding is proposed to reconstruct the pixels of hyperspectral image. The reconstructed pixels characterize the spatial structure. The K-means method is used to form the dictionary, which has stronger representation ability. Finally, the classification...
Based on the idea of SRC(Sparse Representation based Classification), a novel approach HSIC-SRC is proposed in this paper. Unlike most existing algorithms for HSIC(HSI Classification) via sparse representation, our main contributions lie in two aspects, 1) Considering the performance of SRC depending on the quality of dictionary, we employ LC-KSVD(Label Consistent KSVD) algorithm which joints the...
The paper proposes a method of schistosoma cercariae image recognition via sparse representation(IRSR). In the method, all the schistosoma cercariae image training samples compose the dictionary for sparse representation. For each test sample, its projection coefficient in the dictionary is computed and the category which has minimal residual value is assigned to it. We also investigate the effect...
This paper describes a normalization system for text messages to allow them to be read by a TTS engine. To address the large number of texting abbreviations, we use a statistical classifier to learn when to delete a character. The features we use are based on character context, function, and position in the word and containing syllable. To ensure that our system is robust to different abbreviations...
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