Image Segmentation Based on Self-supervised Classification and Multispace KL Transform
基于多子空间KL变换的纹理图像自监督分割方法

This paper presents a texture segmentation algorithm based on self-supervised classification and multispace KL transform.It turns unsupervised clustering into self-supervised classification to decrease the ratio of misclassification.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 multidirection 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.