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10 May 2012 Multisource taxonomy-based classication using the transferable belief model
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Abstract
This paper addresses the problem of multi-source object classication in a context where objects of interest are part of a known taxonomy and the classication sources report at varying levels of specicity. This problem must consider several technical challenges: a) support fusion of heterogeneous classication inputs, b) provide a computationally scalable approach that accommodates taxonomy's with thousands of leaf nodes, and c) provide outputs that support tactical decision aides and are suitable inputs for subsequent fusion processes. This paper presents an approach that employs the Transferable Belief Model, Pignistic Transforms, and Bayesian Fusion to address these challenges.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
William J. Farrell III and Andrew M. Knapp "Multisource taxonomy-based classication using the transferable belief model", Proc. SPIE 8407, Multisensor, Multisource Information Fusion: Architectures, Algorithms, and Applications 2012, 840704 (10 May 2012); https://doi.org/10.1117/12.923873
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