Texture Analysis Experiments with

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, cooccurrence 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. 1. Texture Benchmarks Performance evaluation of texture analysis algorithms is of fundamental importance in image analysis. The ability to rank algorithms based on how well they perform on recognising the surface properties of an image region is crucial to selecting optimal feature extraction methods. However, one must concede that a given texture analysis algorithm may have inherent strengths that are only evident when applied to a specific data set, i.e. no single algorithm is the best for all applications. This does not imply that benchmark evaluation studies are not useful. As synthetic benchmarks are generated to reflect naturally found textures, algorithm performances on these can be analysed to gain an understanding on where the algorithms are more likely to work better. For our study the objective is to compare texture algorithms from feature extraction perspective, and therefore the recognition rate of a classifier trained on these features is an appropriate measure of how well the texture algorithms perform. 1 Singh, S. and Sharma, M. Texture Analysis Experiments with Meastex and Vistex Benchmarks, Proc. International Conference on Advances in Pattern Recognition, Lecture Notes in Computer Science no. 2013, S. Singh, N. Murshed and W. Kropatsch (Eds.), Springer, Rio (11-14 March, 2001). Texture benchmark evaluation is not a new area of work, however previous work has either compared too few algorithms or used very small number of benchmark images that makes it difficult to generalise results (see [15] for a criticism of various studies on performance evaluation). Texture methods used can be categorised as: statistical, geometrical, structural, model-based and signal processing features [17]. Van Gool et al. [18] and Reed and Buf [13] present a detailed survey of the various texture methods used in image analysis studies. Randen and HusØy [12] conclude that most studies deal with statistical, model-based and signal processing techniques. Weszka et al. [20] compared the Fourier spectrum, second order gray level statistics, co-occurrence statistics and gray level run length statistics and found the cooccurrence were the best. Similarly, Ohanian and Dubes [8] compare Markov Random Field parameters, multi-channel filtering features, fractal based features and co-occurrence matrices features, and the co-occurrence method performed the best. The same conclusion was also drawn by Conners and Harlow [2] when comparing run-length difference, gray level difference density and power spectrum. Buf et al. [1] however report that several texture features have roughly the same performance when evaluating co-occurrence features, fractal dimension, transform and filter bank features, number of gray level extrema per unit area and curvilinear integration features. Compared to filtering features [12], co-occurrence based features were found better as reported by Strand and Taxt [14], however, some other studies have supported exactly the reverse. Pichler et al. [10] compare wavelet transforms with adaptive Gabor filtering feature extraction and report superior results using Gabor technique. However, the computational requirements are much larger than needed for wavelet transform, and in certain applications accuracy may be compromised for a faster algorithm. Ojala et al. [9] compared a range of texture methods using nearest neighbour classifiers including gray level difference method, Law's measures, center-symmetric covariance measures and local binary patterns applying them to Brodatz images. The best performance was achieved for the gray level difference method. Law's measures are criticised for not being rotationally invariant, for which reason other methods performed better. In this paper we analyse the performance of five popular texture methods on the publicly available Meastex database [7,15] and Vistex database [19]. For each database we extract five feature sets and train a classifier. The performance of the classifier is evaluated using leave-one-out cross-validated method. The paper is organised as follows. We first present details of the Meastex and Vistex databases. Next, we describe our texture measures for data analysis and then present the experimental details. The results are discussed for the linear and nearest neighboour classifiers. Some conclusions are derived in the final section. 1.1 Meastex Benchmark Meastex is a publicly available texture benchmark. Each image has a size of 512x512 pixels and is distributed in raw PGM format. We split each image into 16 sub-images to increase the number of samples available for each class. The textures are available for classes asphalt, concrete, grass and rock. Finally we get a total of 944 images from which texture features are extracted. Table 1 shows the number of features extracted for each texture method. Table 2 shows the composition of the Meastex database. Feature extraction method No. of features Autocorrelation 99