Unsupervised texture segmentation based on histogram of encoded Gabor features and MRF model

In this paper, we propose an unsupervised texture segmentation scheme in which Gabor transforms and GMRF model are integrated. The Gabor filters are used to extract low-level textural features. The Gabor feature vectors are mapped to an 1-D space using the Kohnen's SOFM algorithm, and then encoded by the feature map indices. The histogram of encoded features over a small window are used to determine the regions of homogeneous textures. From these regions, class-specific parameters for GMRF model are estimated and used to detect exact boundaries of different textures.