IEEE Trans Image Process - Variational region-based segmentation using multiple texture statistics.

Tópicos

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Resumo

This paper investigates variational region-level criterion for supervised and unsupervised texture-based image segmentation. The focus is given to the demonstration of the effectiveness and robustness of this region-based formulation compared to most common variational approaches. The main contributions of this global criterion are twofold. First, the proposed methods circumvent a major problem related to classical texture based segmentation approaches. Existing methods, even if they use different and various texture features, are mainly stated as the optimization of a criterion evaluating punctual pixel likelihoods or similarity measure computed within a local neighborhood. These approaches require sufficient dissimilarity between the considered texture features. An additional limitation is the choice of the neighborhood size and shape. These two parameters and especially the neighborhood size significantly influence the classification performances: the neighborhood must be large enough to capture texture structures and small enough to guarantee segmentation accuracy. These parameters are often set experimentally. These limitations are mitigated with the proposed variational methods stated at the region-level. It resorts to an energy criterion defined on image where regions are characterized by nonparametric distributions of their responses to a set of filters. In the supervised case, the segmentation algorithm consists in the minimization of a similarity measure between region-level statistics and texture prototypes and a boundary based functional that imposes smoothness and regularity on region boundaries. In the unsupervised case, the data-driven term involves the maximization of the dissimilarity between regions. The proposed similarity measure is generic and permits optimally fusing various types of texture features. It is defined as a weighted sum of Kullback-Leibler divergences between feature distributions. The optimization of the proposed variational criteria is carried out using a level-set formulation. The effectiveness and the robustness of this formulation at region-level, compared to classical active contour methods, are evaluated for various Brodatz and natural images.

Resumo Limpo

paper investig variat regionlevel criterion supervis unsupervis texturebas imag segment focus given demonstr effect robust regionbas formul compar common variat approach main contribut global criterion twofold first propos method circumv major problem relat classic textur base segment approach exist method even use differ various textur featur main state optim criterion evalu punctual pixel likelihood similar measur comput within local neighborhood approach requir suffici dissimilar consid textur featur addit limit choic neighborhood size shape two paramet especi neighborhood size signific influenc classif perform neighborhood must larg enough captur textur structur small enough guarante segment accuraci paramet often set experiment limit mitig propos variat method state regionlevel resort energi criterion defin imag region character nonparametr distribut respons set filter supervis case segment algorithm consist minim similar measur regionlevel statist textur prototyp boundari base function impos smooth regular region boundari unsupervis case datadriven term involv maxim dissimilar region propos similar measur generic permit optim fuse various type textur featur defin weight sum kullbackleibl diverg featur distribut optim propos variat criteria carri use levelset formul effect robust formul regionlevel compar classic activ contour method evalu various brodatz natur imag

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