Self-supervised texture segmentation using complementary types of features

Abstract

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 classification of wavelet features. The regions of “high confidence” and “low confidence” are identified based on the MRSAR segmentation result using multilevel morphological erosion. The second-stage classifier is trained by the “high-confidence” samples and is used to reclassify only the “low-confidence” pixels. The proposed approach leverages on the advantages of both MRSAR and wavelet features. Experimental results show that the misclassification error can be significantly reduced by using complementary types of texture features.