Unsupervised Texture Segmentation with Nonparametric Neighborhood Statistics

Abstract. This paper presents a novel approach to unsupervised texture segmentation that relies on a very general nonparametric statistical model of image neighborhoods. The method models image neighborhoods directly, without the construction of intermediate features. It does not rely on using specific descriptors that work for certain kinds of textures, but is rather based on a more generic approach that tries to adaptively capture the core properties of textures. It exploits the fundamental description of textures as images derived from stationary random fields and models the associated higher-order statistics nonparametrically. This general formulation enables the method to easily adapt to various kinds of textures. The method minimizes an entropy-based metric on the probability density functions of image neighborhoods to give an optimal segmentation. The entropy minimization drives a very fast level-set scheme that uses threshold dynamics, which allows for a very rapid evolution to...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Suyash P. Awate, Tolga Tasdizen, Ross T. Whitaker
• Keywords: Computer Vision, ECCV 2006, Image Neighborhoods, Method Models Image, Optimal Segmentation, Probability Density Functions, Unsupervised Texture Segmentation