Abstract: We propose a supervised method for texture segmentation using ant colony optimization algorithm(ACO) to extract prominent Gabor features.Firstly,training samples are randomly selected from the texture images and exposed to the Gabor filters transform.Secondly,a feature subset corresponding to each sample can be selected by minimizing the distance summation between Gabor texture vectors and the different texture classes based on ACO.It aims to determine the central frequency and orientation of Gabor filters globally.The objective makes the frequency responses between different textures well separated.Finally,K-means algorithm is chosen to segment the test images based on reduced features.The experiments on two synthetical textured images show the promising results using the proposed method.