A modified tabu search approach for texture segmentation using 2-D non-separable wavelet frames

The paper proposes a new feature vector which is characterized by a density of 2D overcomplete wavelet transform extrema estimated at the output of the corresponding filter bank and forms a feature vector for clustering. We formulated the texture segmentation problem as a combinatorial optimization. The good texture discrimination ability of the feature is demonstrated with the three-category texture image via a modified tabu search approach. According to the proposed schedule, the trial solution in this search uses the centroid of the cluster as a string and has been performed to make the objective function better in the hope that it eventually will achieve a better solution. A quantitative calculation of the accuracy of our segmentation results is presented.