Serial Multiple Classifier Systems Exploiting a Coarse to Fine Output Coding

We investigate serial multiple classifier system architectures which exploit a hierarchical output coding. Such architectures are known to deliver performance benefits and are widely used in applications involving a large number of classes such as character and handwriting recognition. We develop a theoretical model which underpins this approach to multiple classifier system design and show how it relates to various heuristic design strategies advocated in the literature. The approach is applied to the problem of 3D object recognition in computer vision.