Non-negative kernel sparse coding for image classification

Yungang Zhang*, Tianwei Xu, Jieming Ma

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

1 Citation (Scopus)


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 ‘additive’ nature of non-negative sparse coding, promising classification performance can be obtained.

Original languageEnglish
Title of host publicationIntelligence Science and Big Data Engineering
Subtitle of host publicationImage and Video Data Engineering - 5th International Conference, IScIDE 2015, Revised Selected Papers
EditorsXiaofei He, Zhi-Hua Zhou, Xinbo Gao, Zhi-Yong Liu, Yanning Zhang, Baochuan Fu, Fuyuan Hu, Zhancheng Zhang
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319239873
Publication statusPublished - 2015
Externally publishedYes
Event5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015 - Suzhou, China
Duration: 14 Jun 201516 Jun 2015

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference5th International Conference on Intelligence Science and Big Data Engineering, IScIDE 2015


  • Dictionary learning
  • Image classification
  • Kernel methods
  • Non-negative sparse coding

Cite this