Image Feature Extraction Using Kernel Winner-take-all Based on Independent Component Analysis Algorithm
CHEN Jie1,2,SHANG Li1
1. Institute of Electronic Information Engineering, Suzhou Vocational University, Suzhou, Jiangsu 215104, China
2. School of Urban Rail Translation, Soochow University, Suzhou, Jiangsu 215100, China
Abstract Utilized the property that kernel function learning can solve efficiently the linearly inseparable problem of image features, and combined advantages of sparse representation algorithm, a novel image feature extraction method is proposed. Here the sparse representation of images is realized by the winner-take-all rule based independent component analysis method, which can extract image high-dimension features efficiently and has quicker convergent speed, because it is unnecessary to optimize high-order nonlinear function and estimate sparse density. Compared with the winner-take-all rule based independent component analysis method, the experiment results in PolyU database testify that image features extracted by the combination method of kernel function learning and sparse representation can favor to improve the precision of feature classification.
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2017, Vol. 38 
