This paper proposes a novel method for texture segmentation using independent component analysis (ICA) of Gabor features (called ICAG). It has three distinguished aspects. (1) Gabor wavelets transformation first produces distinct textural features characterized by spatial locality, scale and orientation selectivity. (2) Principal component analysis (PCA) then reduces the dimensionality of these features and ICA finally derives independent features for texture segmentation. (3) Two different frameworks for ICA are discussed. Framework I regards pixels as random variables and represents them as a column vector by re-shaping all the transformed images row-by-row, while framework II treats the statistical features, viz. the mean and standard deviation of image, as random variables. The statistical features of all the transformed images construct a column vector. Comparative experiment results among ICAG, Gabor wavelets and ICA indicate that ICAG provides the best performance and framework II is more efficient and applicable for texture segmentation.