Region-based active contours and sparse representations for texture segmentation

In this paper we propose a rigorous framework for texture image segmentation relying on region-based active contours (RBAC) and sparse texture representation. Such representations allow to efficiently describe a texture by transforming it in a dictionary of appropriate waveforms (atoms) where the texture representation coefficients are concentrated on a small set. For segmentation purposes. these atoms have to be multiscale and localized both in space and frequency, e.g. the wavelet transform. To discriminate different textures, we measure a ldquodistancerdquo between the non-parametric Parzen estimates of their respective sparse-representation coefficients probability density functions (pdfs). These distance measures are then used within RBAC, and we take benefit from shape derivative tools to derive the evolution speed expression of the RBAC. Our framework is applied to both supervised (with reference textures), and unsupervised texture segmentation. A series of experiments on synthetic textures illustrate the potential applicability of our method.