In view of the fact that the traditional SLIC (simple linear iterative clustering) superpixel segmentation algorithm does not comprehensively consider the texture information characteristics of the image,which leads to the problem s of insensitive edge detection and unsatisfactory segmentation effect when supe rpixel segmentation is performed on images with strong edge information and comp lex texture.So we propose to extract texture feature components from the origin al image through noise suppression,and construct a similarity measurement metho d that combines color features,texture features and spatial location features. The improved method improves the sensitivity of edge detection,and enhances the robustness of the algorithm when segmenting images with strong edge information and complex textures.In addition,a spiral search method is proposed for clust ering,which speeds up the convergence speed of the algorithm and improves the e fficiency of segmentation.The improved method is tested on the BSDS500public d ata set.The results show that the improved method outperforms the traditional a lgorithm in four indicators:edge recall rate,under-segmentation error rate,c omplete segmentation accuracy and algorithm running time.