Multichannel texture image segmentation using local feature fitting based variational active contours

We study an efficient texture image segmentation model for multichannel images using a local feature fitting based active contours scheme. Using a chromaticity-brightness decomposition, we propose a flexible segmentation approach using multi-channel texture and intensity in a globally convex continuous optimization framework. We make use of local feature histogram based weights with the smoothed gradients from the brightness channel and localized fitting for the chromaticity channels. A fast numerical implementation is described using an efficient dual minimization formulation and experimental results on synthetic and real color images indicate the superior performance of the proposed method compared to related approaches. The novel contributions include the use of local feature density functions in the context of a luminance-chromaticity decomposition combined with a globally convex active contour variational method to capture texture variations for image segmentation.