Cluster Coding Algorithm for Stochastic Textures Identification

A stochastic texture is a texture whereby the arrangement of the pattern is random in nature. The identification for each of these textures is uncertain and usually involves complex methods to search for the locations of the correlation between these textures. We propose the cluster coding algorithm which could easily classify the stochastic textures in a semantic way based on statistical features. This algorithm is successfully applied in both synthetic and real-life textures for segmentation. In this study, the cluster coding showed a significant improvement over other techniques in terms of classification accuracy and computation time.