Supervised texture segmentation using DWT and a modified K-NN classifier

We present a texture segmentation scheme based on the discrete wavelet transform (DWT). The DWT is a non-redundant representation which can reduce computational complexity in the processing. The texture segmentation scheme presented here consists of three steps: feature extraction, conditioning, and clustering. For feature conditioning, a number of smoothing windows have been tested. Clustering is performed with a modified k-nearest neighbour clustering algorithm. The proposed scheme consistently achieves error rates of less than 10% with the best average error of 5.62%