Comparing Texture Analysis Methods through Classification

The development and testing of two techniques of texture analysis based on different mathematical tools—the semivariogram and the Fourier spectra—are presented. These are also compared against a benchmark approach: the Gray-Level Co-occurrence Matrix. The three methods and their implementation are briefly described. Three series of experiments have been prepared to test the performance of these methods in various classification contexts. These contexts are simulated by varying the number, type and visual likeness of the texture patches used in classification tests. More specifically, their ability to correctly classify, separate, and associate texture patches is assessed. Results suggest that the classification context has an important impact on performance rates of all methods. The variogram-based and the Gray-Tone Dependency Matrix methods were generally superior, each one in particular contexts.