@inproceedings{f3ce1f613d0c41c29fe8b9d3979de31a,
title = "Maximum constrained sparse coding for image representation",
abstract = "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{\^a}{\texttrademark} 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{\^a}{\texttrademark} 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.",
keywords = "Sparse coding, image representation, infinite norm, maximum constraint",
author = "Jie Zhang and Danpei Zhao and Zhiguo Jiang",
note = "Publisher Copyright: {\textcopyright} 2015 SPIE.; 9th International Symposium on Multispectral Image Processing and Pattern Recognition, MIPPR 2015 ; Conference date: 31-10-2015 Through 01-11-2015",
year = "2015",
doi = "10.1117/12.2204911",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Jianguo Liu and Tianxu Zhang",
booktitle = "MIPPR 2015",
address = "美国",
}