A comparative study of two different multiple expert architectures for robust object recognition

A generalised application area of machine vision is in the classification of different objects based on specified criteria. Applications of this nature are encountered more and more often in real industrial situations and the need to design robust classification architectures is now being felt more intensely than ever before. In designing such systems, it is being increasingly realised that judicious combination of multiple experts forming an integral configuration can achieve a higher overall performance than any of the individual experts on its own.(1) Many configurations, taking advantage of different individual strengths of different experts, have been investigated.(2-4) One particular class of structure seeks to exploit the a priori knowledge about the behaviour of a particular basic classifier on a particular reference database and uses that information to form a hierarchical classification structure that treats the structurally similar and dissimilar objects separately.(5,6) The basic classifier performs an initial separation of the input objects. Based on a priori knowledge, initially separated objects are regrouped to form structurally similar groups, incorporating objects that have a high probability of being confused. A number of such groups having two or three classes in each group can be formed. The structurally dissimilar objects are classified using a generalised classifier. On the other hand, the different groups formed in the previous stage undergo group-wise classification. The final decision of the classifier structure is formed by combining the decisions of the generalised classifier and the specialised group-wise classifiers.