Texture features are among the most commonly used image attributes in image understanding applications, such as image retrieval from databases. A number of methods and their variants have been developed over the years for texture feature extraction. Whereas they all have their merits and flaws, it is worthwhile to evaluate their performance in a specific application domain. The goal here is to establish which texture features are better suited for segmentation of natural scenes that contain multiple natural and synthetic textures. The performance of unsupervised texture segmentation based on multiresolution simultaneous autoregressive (MRSAR) models, wavelet coefficients, fractal dimension, edge direction and magnitude, and color moments is examined.

Publication Date



"Evaluation of texture features for image segmentation," Presented at the IEEE Western New York Image Processing Workshop 2001. The Institute of Electrical and Electronics Engineers. Held at the University of Rochester: Rochester, New York: 14 September 2001. ©2001 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type


Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus