Rock image classification based on k-nearest neighbour voting

Image classification is usually based on various visual descriptors extracted from the images. The descriptors characterising, for example, image colours or textures are often high dimensional and their scaling varies significantly. In the case of natural images, the feature distributions are often non-homogenous and the image classes are also overlapping in the feature space. This can be problematic, if all the descriptors are combined into a single feature vector in the classification. A method is presented for combining different visual descriptors in rock image classification. In our approach, k-nearest neighbour classification is first carried out for each descriptor separately. After that, the final decision is made by combining the nearest neighbours in each base classification. The total numbers of the neighbours representing each class are used as votes in the final classification. The experimental results with rock image classification indicate that the proposed classifier combination method is more accurate than the conventional plurality voting.