Improved algorithm of image feature extraction based on independent component analysis
Zhao Liu · Guangdian gongcheng · 2007
In this paper, an improved algorithm of image feature extracting for independent component analysis was proposed based on basic functions’ maximization of sparseness. Starting from a Laplacian Priori of the image, the ICA problem was boiled down to a minimum of L1 norm problem, but the problem would be much easier to solve by searching a maximum of its dual space L∞ norm. This method avoids the expensive optimization of high-order non-linear contrast function, which can be commonly found in other ICA methods. The simulation results show the proposed method has sparser and faster convergence than others.