Supervised texture segmentation using DT-CWT and a modified k-NN classifier

Texture segmentation has been an important problem in image processing. Filtering approaches have been popular, and recent studies have indicated a need for efficient, low-complexity algorithms. In this paper, we present a texture segmentation scheme based on the Dual-Tree Complex Wavelet Transform (DT-CWT). The advantage of the DT-CWT over other approaches is that it offers a partially redundant representation with strong directionality. The texture segmentation scheme presented here consists of three steps: feature extraction, conditioning, and classification. A number of feature smoothing windows have been tested. Classification is performed using a modified K-NN clustering algorithm. The proposed scheme consistently achieves error rates of less than 10%.