Unsupervised Texture Segmentation Based on Geodesic Active Regions

Abstract

This paper proposes an algorithm based on curve evolution for unsupervised texture segmentation. A multidimensional feature space is achieved by using a Gabor filter bank to extract texture features. To avoid deforming contours directly in a vector-valued space, a Gaussian mixture model (GMM) is used to describe the statistical distribution of the space and get the boundary and region probabilities. Then a framework of geodesic active regions is applied based on them to get final results. In the end, the experimental results demonstrate that this method can obtain satisfied boundaries between different texture regions.