Multiple Classifier System Approach to Model Pruning in Object Recognition

We propose a multiple classifier system approach to object recognition in computer vision. The aim of the approach is to use multiple experts successively to prune the list of candidate hypotheses that have to be considered for object interpretation. The experts are organised in a serial architecture, with the later stages of the system dealing with a monotonically decreasing number of models. We develop a theoretical model which underpins this approach to object recognition and show how it relates to various heuristic design strategies advocated in the literature. The merits of the advocated approach are then demonstrated experimentally using the SOIL database. We show how the overall performance of a two stage object recognition system, designed using the proposed methodology, improves. The improvement is achieved in spite of using a weak recogniser for the first (pruning) stage. The effects of different pruning strategies are demonstrated.