A two-stage texture segmentation approach is proposed where an initial segmentation map is obtained through unsupervised clustering of multiresolution simultaneous autoregressive (MRSAR) features and is followed by self-supervised or bootstrapped classification of wavelet features. The self-supervised stage is based on a segmentation confidence map, where the regions of “high confidence” and “low confidence” are identified on the MRSAR segmentation result using multilevel morphological erosion. The second-stage wavelet classifier is trained from the “high-confidence” samples and is used to reclassify only the “low-confidence” pixels. The final reclassification is based on rules that combine minimum distance and spatial constraints. Additionally, an improved coefficient feature normalization procedure is used during the classification process of both stages. The proposed two-stage approach leverages on the advantages of both MRSAR and wavelet features, and incorporates an adaptive neighborhood-based spatial constraint. Experimental results show that the misclassification error can be significantly reduced compared to morphological cleaning operations alone.

Publication Date



"Two-stage texture segmentation using complementary features," Proceedings of the 2000 International Conference on Image Processing (ICIP '00). The Institute for Electrical and Electronics Engineers. Held in Vancouver, British Columbia, Canada: 10-13 September 2000. ©2000 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.ISBN:0-7803-6297-7Note: 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