Unsupervised Texture Segmentation Using Feature Distributions

This paper presents an unsupervised texture segmentation method, which uses distributions of local binary patterns and pattern contrasts for measuring the similarity of adjacent image regions during the segmentation process. Nonparametric log-likelihood test, the G statistic, is engaged as a pseudo-metric for comparing feature distributions. A region-based algorithm is developed for coarse image segmentation and a pixelwise classification scheme for improving localization of region boundaries. The performance of the method is evaluated with various types of test images. 1999 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.

• Type: Conference
• Year: 1997
• Where: ICIAP
• Authors: Timo Ojala, Matti Pietikäinen
• Keywords: Coarse Image Segmentation, ICIAP 1997, Image Processing, Nonparametric Log-likelihood Test, Texture Segmentation Method