Two-stage texture segmentation using complementary features

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