Classification of nonhomogenous texture images by combining classifiers

Most of the natural textures are nonhomogenous. In nonhomogenous texture images, the textural features may have strong variations. These variations cause errors in the classification of these images. In this paper we present a novel method for classification of the nonhomogenous textures. The classification method is based on the combination of separate classifiers. The outputs of the separate classifiers are collected into a classification result vector (CRV). This vector is used in the final classification of the texture samples. Using this method, the classification errors caused by variations of feature values can be minimized. The method is tested using nonhomogenous rock texture images. The results show that our method is suitable for classifying nonhomogenous texture samples. It also gives better classification results than the commonly used methods for combining classifiers.