SVM ensemble approaches for improving texture classification performance based on complex network model with spatial information

This paper proposes an SVM ensemble approach for improving textural classification performance. Finding informative patterns in an image texture is an important issue for image classification and remains a challenge. Local spatial pattern mapping (LSPM) method has been proposed for texture classification based on a complex network model. The purpose of the method was manipulating the spatial distribution in an image texture with multi-radial distance analysis. Although the classification performance was improved, there is a limitation by using a single support vector machine (SVM) as a classifier. Accordingly, we propose an SVM ensemble method with Bagging technique to overcome the limitation by showing improved textural classification performance. In experiments, the SVM ensemble classification performance is compared to the single SVM, SVM with cross-validation and k-NN classifiers by using the Brodatz, UIUC and Outex texture databases. As results, the SVM ensemble is shown to be effective for improving textural classification performance as compared to the other classifiers.