Kernel Feature Extraction Approach for Color Image Recognition

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.

• Periodical: Advanced Materials Research (Volumes 760-762)
• Pages: 1621-1626
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.760-762.1621
• Online since: September 2013
• Authors: Xiao Yuan Jing, Kun Li, Song Song Wu, Yong Fang Yao, Chao Wang
• Keywords: Canonical Correlation Analysis, Color Image, Discriminant Analysis, Feature Extraction, Kernel Method

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