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.