Image Feature Extraction Based on Kernel ICA
Wenzhi Liao, Jinshan Jiang · 2008
A new feature extraction approach based on kernel independent component analysis (Kernel ICA) is proposed in this paper. The Kernel ICA is applied to learn basis vector for feature extraction, and then the basis vector is used as a template model to extract the edge feature from the testing images which are completely different from the training image. The simulating experiment shows that the approach proposed in this paper has a better performance than ICA.