Extracting effective features for texture classification is always a difficult problem in texture analysis. We present a method for texture features extraction by independent component analysis (ICA) of Gabor features (called ICAG). It has three distinguished aspects. First, Gabor wavelet transformation first produces distinct textural features characterized by spatial locality, scale, and orientation selectivity. Second, principal component analysis (PCA) and ICA then reduce the dimensionality and redundancy of these features. Thus, the resulting independent components are taken as texture features for classification. Third, in the ICA procedure, two different frameworks are discussed. Framework I, called ICAG I, regards pixels as random variables and represents them as a column vector by reshaping all the transformed images row by row, while framework II, called ICAG II, treats the statistical features, namely, the mean and standard deviation, as random variables. Thus, the statistical features of all the transformed images construct a column vector. Finally, comparative experiments between ICAG and the other traditional methods—ICA and Gabor wavelets—are performed on two composite images (five narrowband textures and five natural textures with broader bands). The results indicate that ICAG provides the best performance and ICAG II is more efficient and applicable.