This paper presents a texture segmentation algorithm based on self-supervised classification and multispace KL transform. It turns unsupervised clustering into self-supervlsed classification to decrease the ratio of misclassificatlon. Our algorithm adopts a multispace method for feature selection to avoid the limitations introduced by supposing that all samples obey a single Gauss distribution. Firstly multldirection and multiscale Gabor transforms are applied to target texture images ; then fuzzy C means clustering is acted on the results of above transforms to extract some typical training samples

which are requested to supervise later segmentation. Secondly a separate subspace for each class is initialized by training samples respectively. Lastly other samples are classified with multispace KL transforms through the iterative processes. Our algorithm is fully competent for various composite texture segmentations. And experimental results have proved that it can successfully reduce misclassification ratio in the same time improve the visual effects of texture segmentation.