Texture Analysis Experiments with Meastex and Vistex Benchmarks

TLDR: This paper investigates how well the following texture extraction methods perform: autocorrelation, co-occurrence matrices, edge frequency, Law's, and primitive length and aims to determine whether by combining them into a single feature set will have a significant impact on the overall recognition performance.

Abstract: The analysis of texture in images is an important area of study. Image benchmarks such as Meastex and Vistex have been developed for researchers to compare their experiments on these texture benchmarks. In this paper we compare five different texture analysis methods on these benchmarks in terms of their recognition ability. Since these benchmarks are limited in terms of their content, we have divided each image into n images and performed our analysis on a larger data set. In this paper we investigate how well the following texture extraction methods perform: autocorrelation, co-occurrence matrices, edge frequency, Law's, and primitive length. We aim to determine if some of these methods outperform others by a significant margin and whether by combining them into a single feature set will have a significant impact on the overall recognition performance. For our analysis we have used the linear and nearest neighbour classifiers.