zbMATH — the first resource for mathematics

Dempster-Shafer models for object recognition and classification. (English) Zbl 1096.62065
Summary: We consider situations in which each individual member of a defined object set is characterized uniquely by a set of variables, and we propose models and associated methods that ‘recognize’ or ‘classify’ a newly observed individual. Inputs consist of uncertain observations on the new individual and on a memory bank of previously identified individuals. Outputs consist of uncertain inferences concerning degrees of agreement between the new object and previously identified objects or object classes, with inferences represented by Dempster-Shafer belief functions.
We illustrate the approach using models constructed from independent simple support belief functions defined on binary variables. In the case of object recognition, our models lead to marginal belief functions concerning how well the new object matches objects in memory. In the classification model, we compute beliefs and plausibilities that the new object lies in defined subsets of an object set. When regarded as similarity measures, our belief and plausibility functions can be interpreted as candidate membership functions in the terminology of fuzzy logic.

62H30 Classification and discrimination; cluster analysis (statistical aspects)
68T10 Pattern recognition, speech recognition
65C60 Computational problems in statistics (MSC2010)
Full Text: DOI
[1] Neural networks for pattern recognition. New York: Oxford University Press; 1995.
[2] Ars conjectandi. Basel: Thurnisiorum; 1713.
[3] Dempster, J Am Stat Assoc 99 pp 882– (2004)
[4] A mathematical theory of evidence. Princeton, NJ: Princeton University Press; 1976. · Zbl 0359.62002
[5] Dempster, Ann Math Stat 38 pp 325– (1967)
[6] Construction and local computation aspects of network belief functions. In: , editors. Influence diagrams, belief nets and decision analysis. Chichester: John Wiley; 1990.
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.