Title: Image classification dictionary learning based on sparse representation
Abstract:In order to deal with the weak structure of dictionary in the K-SVD algorithm,an nonlocal classification dictionary learning method( NLC-DL) based on sparse representation was proposed by taking advan...In order to deal with the weak structure of dictionary in the K-SVD algorithm,an nonlocal classification dictionary learning method( NLC-DL) based on sparse representation was proposed by taking advantage of image nonlocal self-similarity. The method clustered image patches with structural similarity by the K-means algorithm,then the dictionaries for each class were learned to reinforce the effectiveness. The sparse coefficients obtained by the Orthogonal Matching Pursuit algorithm( OMP) were used to optimize all the dictionaries alternately. Both the sparse coefficients and the optimized dictionaries were used for reconstructing the true image. Experimental results showed that the obtained dictionaries achieved a better effect with less error on representing the training sample and maintained the structural information effectively. Furthermore,the proposed method for reconstructing images performed better than the traditional ones in terms of PSNR and visual effect.Read More
Publication Year: 2015
Publication Date: 2015-01-01
Language: en
Type: article
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