Unsupervised texture segmentation based on histogram of encoded Gabor features and MRF model
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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.
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Proceedings 1999 International Conference on Image Processing (Cat. 99CH36348)
