A DATA-CENTRIC APPROACH TO UNSUPERVISED TEXTURE SEGMENTATION USING PRINCIPLE REPRESENTATIVE PATTERNS

Abstract

Features that capture textural patterns of a certain class of images are crucial for texture segmentation tasks. This paper introduces a data-centric approach to efficiently extract and represent textural information, which adapts to a wide variety of textures. Based on the strong self-similarities and quasi periodicity in texture images, the proposed method first constructs a representative texture pattern set for the given image by leveraging the patch clustering strategy. Then, pixel wise texture features are designed according to the similarities between local patches and the representative textural patterns. Moreover, the proposed feature is generic and flexible, and can perform segmentation task by integrating it into various segmentation approaches easily. Extensive experimental results on both textural and natural image segmentation show that the segmentation method using the proposed features achieves very competitive or even better performance compared with the stat-of-the-art methods.