An adaptive region based color texture segmentation using fuzzified distance metric

Abstract

Texture is an important spatial feature, useful for identifying object or region of interest. In texture analysis the foremost task is to extract texture features, which efficiently embody the information about the textural characteristics of the image. This can be used for the segmentation of different texture images. Texture segmentation is the process of partitioning an image into regions with different textures containing similar group of pixels. This paper, mainly focus on the unsupervised segmentation of color textured images as a prototypical application in computer vision. Here the objective is to group pixels or small image patches such that meaningful regions of identical/similar texture are obtained. This paper presents a new approach for color texture segmentation using Haralick features extracted from color co-occurrence matrices. The originality of this approach is to select the most discriminating color texture features extracted from the color co-occurrence matrices calculated in HSI color space. Fuzzified distance metric is used for achieving texture segmentation.