Kernel Feature Extraction Approach for Color Image Recognition

DOI

10.2991/iccsee.2013.726How to use a DOI?

Keywords

color image, feature extraction, kernel method, canonical correlation analysis, discriminant analysis

Abstract

Color Image Recognition is one of the most important fields in Pattern Recognition. Both Multi-set canonical correlation analysis and Kernel method are important techniques in the field of color image recognition. In this paper, we combine the two methods and propose one novel color image recognition approach: color image kernel canonical correlation analysis (CIKCCA). Color image kernel canonical correlation analysis is based on the theory of multi-set canonical correlation analysis and extracts canonical correlation features among the color image components. Then fuse the features of the color image components in the feature level, which are used for classification and recognition. Experimental results on the FRGC-v2 public color image databases demonstrate that the proposed approach acquire better recognition performance than other color recognition methods.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoyuan Jing
AU  - Kun Li
AU  - Songsong Wu
AU  - Yongfang Yao
AU  - Chao Wang
PY  - 2013/03
DA  - 2013/03
TI  - Kernel Feature Extraction Approach for Color Image Recognition
BT  - Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)
PB  - Atlantis Press
SP  - 2909
EP  - 2913
SN  - 1951-6851
UR  - https://doi.org/10.2991/iccsee.2013.726
DO  - 10.2991/iccsee.2013.726
ID  - Jing2013/03
ER  -