Serial combination of multiple experts: A unified evaluation
Multiple expert decision combination has received much attention in recent years. This is a multi-disciplinary branch of pattern recognition which has extensive applications in numerous fields including robotic vision, artificial intelligence, document processing, office automation human-computer interfaces, data acquisition, storage and retrieval, etc. In recent years, this application area has been extended to forensic science, including the identification of individuals using measures depending on biometrics, security and other applications. In this paper, a generalised multi expert multi-level decision combination strategy, the serial combination approach, has been investigated from the dual viewpoints of theoretical analysis and practical implementation. Different researchers have implicitly utilised various approaches based on this concept over the years in a wide spectrum of application domains, but a comprehensive, coherent and generalised presentation of this approach from both theoretical and implementation viewpoints has not been attempted. While presenting here a unified framework for serial multiple expert decision combination, it is shown that many multi-expert approaches reported in the literature can be easily represented within the proposed framework. Detailed theoretical and practical discussions of the various performance results with these combinations, analysis of the internal processing of this approach, a case study for testing the theoretical framework, issues relating to processing overheads associated with the implementation of this approach, general comments on its applicability to various task domains and the generality of the approach in terms of reevaluating previous research have also been incorporated.
character recognition generic serial framework multiple expert configurations