Unsupervised texture segmentation using feature selection and fusion

This paper describes a method of unsupervised color texture segmentation by efficiently combining different features obtained from multi-channel and multi-resolution filters. The DWT and DCT features are extracted separately from 3 color bands of the image and then fused together for optimal performance. The features are then ranked according to a selection criteria. We propose a new correlation measure for the task of feature ranking. To select the best combination of features to be used, we use the property of cluster scatter of a selected set of features. Finally, the optimum number of ranked order features are used for segmentation using a Fuzzy C-Means classifier. The performance of the proposed segmentation method is verified using standard benchmark datasets. ©2009 IEEE.