Spatial Texture Analysis: A Comparative Study

Image texture analysis has received a considerable amount of attention over the last few years as it forms the basis of most object recognition methods. It has been suggested by a number of researchers that the spatial methods of texture analysis are superior than frequency domain methods. In this paper we compare some of the traditional, and some fairly new techniques of texture analysis on the MeasTex and VisTex benchmarks to illustrate their relative abilities. The methods considered include autocorrelation (ACF), cooccurrence matrices (CM), edge frequency (EF), Law's masks (LM), run length (RL), binary stack method (BSM), texture operators (TO), and texture spectrum (TS). In addition, we illustrate the advantage of using feature selection on a combined set that improves the overall recognition performance. Keywords : texture, recognition rate, comparison, benchmark

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Maneesha Singh, Sameer Singh
• Keywords: Computer Vision, ICPR 2002, Image Texture Analysis, Texture Analysis, Texture Operators