14 December 2015 Maximum constrained sparse coding for image representation
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Proceedings Volume 9813, MIPPR 2015: Pattern Recognition and Computer Vision; 98130V (2015) https://doi.org/10.1117/12.2204911
Event: Ninth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2015), 2015, Enshi, China
Sparse coding exhibits good performance in many computer vision applications by finding bases which capture highlevel semantics of the data and learning sparse coefficients in terms of the bases. However, due to the fact that bases are non-orthogonal, sparse coding can hardly preserve the samples’ similarity, which is important for discrimination. In this paper, a new image representing method called maximum constrained sparse coding (MCSC) is proposed. Sparse representation with more active coefficients means more similarity information, and the infinite norm is added to the solution for this purpose. We solve the optimizer by constraining the codes’ maximum and releasing the residual to other dictionary atoms. Experimental results on image clustering show that our method can preserve the similarity of adjacent samples and maintain the sparsity of code simultaneously.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jie Zhang, Jie Zhang, Danpei Zhao, Danpei Zhao, Zhiguo Jiang, Zhiguo Jiang, "Maximum constrained sparse coding for image representation", Proc. SPIE 9813, MIPPR 2015: Pattern Recognition and Computer Vision, 98130V (14 December 2015); doi: 10.1117/12.2204911; https://doi.org/10.1117/12.2204911


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