Fusion of possibilistic sources of evidences for pattern recognition

Information processing in modern pattern recognition systems is becoming increasingly complex due to the flood of data and the need to deal with different aspects of information imperfection. In this paper a simple and efficient possibilistic evidential method is defined, taking account of data heterogeneity, combined with proportional conflict redistribution to include information conflict, paradox, and scarcity, within a fusion framework. It ponders information constraints and updating for dynamic fusion, and appropriately considers training set elements imperfection, class set continuity, and system output information scalability, encompassing a significant range of issues encountered in current databases. One example of knowledge sources processing with those constraints is given to explain the main processing phases, followed by suitable application instances in satellite and medical image recognition.