Improving the descriptors extracted from the co-occurrence matrix by preprocessing approaches


In this paper we investigate the effects that different preprocessing techniques have on the performance of features extracted from Haralick’s co-occurrence matrix, one of the best known methods for analyzing image texture. Additionally, we compare and combine different strategies for extracting descriptors from the co-occurrence matrix. We propose an ensemble of different preprocessing methods, where, for each descriptor, a given Support Vector Machine (SVM) classifier is trained. The set of classifiers is then combined by weighted sum rule. The best result is obtained by combining the extracted descriptors using the following preprocessing methods: wavelet decomposition, local phase quantization, orientation, and the Weber law descriptor. Texture descriptors are extracted from the entire co-occurrence matrix as well as from subwindows and evaluated at multiple scales. We validate our approach on eleven image datasets representing different image classification problems using the Wilcoxon signed rank test. Results show that our approach improves the performance of standard methods. All source code for the approaches tested in this paper will be available at:

Keywords Eye detection; face detection; texture descriptors; local phase quantization; feature combination; support vector machine.

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