Combining Classifiers in Rock Image Classification – Supervised and Unsupervised Approach

Combining classifiers has proved to be an effective solution to several classification problems in pattern recognition. In this paper we use classifier combination methods for the classification of natural images. In the image classification, it is often beneficial to consider each feature type separately, and combine the classification results in the final classifier. We present a classifier combination strategy that is based on classification result vector, CRV. It can be applied both in supervised and unsupervised way to the image classification. Natural images are often non-homogenous, which means that there are clearly visible changes in their visual properties. This makes them difficult to classify. In this paper we apply our classifier combination method to the classification of rock images. These images represent real rock image data that is non-homogenous in terms of its color and texture properties. The classification results are compared to the results of commonly used classifier combination strategies.