Multi-level pixel-based texture classification through efficient prototype selection via normalized cut

This paper presents a new efficient technique for supervised pixel-based classification of textured images. A prototype selection algorithm that relies on the normalized cut criterion is utilized for automatically determining a subset of prototypes in order to characterize each texture class at the local level based on the outcome of a multichannel Gabor filter bank. Then, a simple minimum distance classifier fed with the previously determined prototypes is used to classify every image pixel into one of the given texture classes. Multi-sized evaluation windows following a top-down approach are used during classification in order to improve accuracy near frontiers of regions of different texture. Results with standard Brodatz, VisTex and MeasTex compositions and with complex real images are presented and discussed. The proposed technique is also compared with alternative texture classifiers.