Texture Segmentation by Unsupervised Learning and Histogram Analysis Using Boundary Tracing

Abstract

Texture analysis is an important technique in many image processing areas, such as scene segmentation, object recognition, and shape&depth perception. However, most methods are restricted to issue of computational complexity and supervised problems. Accordingly, we propose a efficient method of segmenting texture that uses unsupervised learning schemes to discover a texture cluster without a pre-knowledge. This method applies 2D Gaussian filters to the clustered region iteratively, and the thresholding value for segmenting is automatically determined by analyzing histogram of the clustered inner-region. It can be acquired by the boundary tracing in the clustered region. In order to show the performance of the proposed method, we have attempted to build a various texture images, and the segmenting quality was measured according to the goodness based on the segmented shape of region. Our experimental results showed that the performance of the proposed method is very successful.