An effective color texture image segmentation algorithm based on hermite transform

Abstract

Abstract In this paper, an efficient color texture image segmentation approach is proposed. The proposed approach uses color and texture information independently. The color information is obtained by converting the RGB color space to Luv color space and each color component is considered as a color descriptor. For texture descriptors, Hermite transform is considered. Hermite transform uses the Hermite filters which are formed by the product of Hermite polynomials with Gaussian function. Instead of using all Hermite filters, a filter selection process is adopted to obtain optimal filters. A feature image is constructed based on the magnitude of each filter response. A region smoothing procedure is employed for both the color components and the feature image in order to make the region smoother while preserving the edge information. To this end, weighted least square edge-preserving filtering is used. Comprehensive experiments were conducted to demonstrate the efficiency of the proposed method, using the Berkeley segmentation dataset.