In this paper, we propose a cooperative strategy for segmentation of texture images which integrates reduced Gabor features and image components. In contrast with the structure tensor method, our algorithm can extract more important features for segmentation. In this work, Gabor filters tuned to a set of orientations, scales and frequencies are used to extract texture local features, and the vector-valued active contour without edges model is employed to segment images. The main contribution of this work is the cooperation of image components and the reduced Gabor features which are extracted by principal components analysis (PCA) to represent image features. This cooperation improves the quality of the method, since the segmentation is faster and better. We demonstrate the effectiveness of our algorithm by comparing with the method proposed by Wang for segmenting synthetic and nature texture images.