A Method for Texture Classification by Integrating Gabor Filters and ICA
Runsheng Wang · Dianzi xuebao · 2007
Extracting effective features for texture description and classification is always a difficult problem in texture analysis.This paper proposes a method for texture feature extraction by integrating Gabor filters and independent component analysis(ICA).That is,the texture image is firstly filtered by a given bank of Gabor filters,and then higher-dimensional feature vectors are constructed from the filtered images.Next,the dimensionality of these vectors is reduced by means of principal component analysis(PCA).Finally,the independent components in the resulting vectors with dimensionality reduced are analyzed and extracted by using ICA for texture classification.Comparative experiments among this approach,the classic Gabor filters and ICA,are performed.The results demonstrate and evaluate its performance.