A Novel Active Contour Model for Texture Segmentation

TL;DR

This paper introduces a new unsupervised texture segmentation method using active contours and covariance matrix features, leveraging geodesic distances on the PD(n) manifold to improve segmentation accuracy.

Contribution

The paper presents a novel active contour-based algorithm that employs covariance matrices and geodesic distances for effective texture segmentation.

Findings

Effective segmentation on artificial textures

Successful application to real-world textures

Improved dissimilarity measurement using geodesic distances

Abstract

Texture is intuitively defined as a repeated arrangement of a basic pattern or object in an image. There is no mathematical definition of a texture though. The human visual system is able to identify and segment different textures in a given image. Automating this task for a computer is far from trivial. There are three major components of any texture segmentation algorithm: (a) The features used to represent a texture, (b) the metric induced on this representation space and (c) the clustering algorithm that runs over these features in order to segment a given image into different textures. In this paper, we propose an active contour based novel unsupervised algorithm for texture segmentation. We use intensity covariance matrices of regions as the defining feature of textures and find regions that have the most inter-region dissimilar covariance matrices using active contours. Since covariance matrices are symmetric positive definite, we use geodesic distance defined on the manifold of symmetric positive definite matrices PD(n) as a measure of dissimlarity between such matrices. We demonstrate performance of our algorithm on both artificial and real texture images.