Classification of non-homogenous images using classification probability vector

Combining classifiers has proved to be an effective solution to several classification problems. In this paper, we present a classifier combination strategy that is based on classification probability vector, CPV. In this approach, each visual feature extracted from the image is first classified separately, and the probability distributions provided by separate classifiers are used as a basis of final classification. This approach is particularly suitable for images with non-homogenous and overlapping feature distributions.