Variational Region-Based Segmentation Using Multiple Texture Statistics

This paper addresses variational supervised texture segmentation. The main contributions are twofold. First, the proposed method circumvents 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 local neighborhood. The former approaches require sufficient dissimilarity between used feature statistics. The latter involve an additional limitation which 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 warrant segmentation accuracy. These parameters are often set experimentally. To address these limitations, the proposed method is stated at the region-level, both for...

• Type: Journal
• Year: 2010
• Where: TIP
• Authors: Imen Karoui, Ronan Fablet, Jean-Marc Boucher, Jean-Marie Augustin
• Keywords: Education, Neighborhood Size, Texture Features, TIP 2010, Variational Criterion