@inproceedings{a97625f28f9d41f4b7a80114b37e2dae,
title = "Non-negative kernel sparse coding for image classification",
abstract = "Sparse representation of signals have become an important tool in computer vision. In many applications in computer vision, such as image denoising, image super-resolution and object recognition, sparse representations have produced remarkable performance. In this paper, we propose a non-linear non-negative sparse coding model NNK-KSVD. The proposed model extended the kernel KSVD by embedding the nonnegative sparse coding. Experimental results show that by exploiting the non-linear structure in images and utilizing the {\textquoteleft}additive{\textquoteright} nature of non-negative sparse coding, promising classification performance can be obtained.",
keywords = "Dictionary learning, Image classification, Kernel methods, Non-negative sparse coding",
author = "Yungang Zhang and Tianwei Xu and Jieming Ma",
note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 ; Conference date: 14-06-2015 Through 16-06-2015",
year = "2015",
doi = "10.1007/978-3-319-23989-7_54",
language = "English",
isbn = "9783319239873",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "531--540",
editor = "Xiaofei He and Zhi-Hua Zhou and Xinbo Gao and Zhi-Yong Liu and Yanning Zhang and Baochuan Fu and Fuyuan Hu and Zhancheng Zhang",
booktitle = "Intelligence Science and Big Data Engineering",
}