Variational Region-Based Segmentation Using Multiple Texture Statistics

Type Article
Date 2010-12
Language English
Author(s) Karoui Imen1, 2, Fablet Ronan1, Boucher Jean-Marc1, Augustin Jean-Marie2
Affiliation(s) 1 : Telecom Bretagne, UMR CNRS Lab, F-29238 Brest 3, France.
2 : Inst Francais Rech Exploitat Mer, F-29280 Plouzane, France.
Source Ieee Transactions On Image Processing (1057-7149) (Ieee-inst Electrical Electronics Engineers Inc), 2010-12 , Vol. 19 , N. 12 , P. 3146-3156
DOI 10.1109/TIP.2010.2071290
WOS© Times Cited 21
Keyword(s) Active regions, level sets, nonparametric distributions, supervised and unsupervised segmentation, texture similarity measure
Abstract 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.
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