In this paper a new method for image retrieval using high level color semantic features is proposed. It is based on extraction of low level color characteristics and their conversion into high level semantic features using Johannes Itten theory of color, Dempster-Shafer theory of evidence and fuzzy production rules.

The semantics of similarity measures is studied and reduced to the evidence theory of Dempster and Shafer. Applications are given for classification and configuration, the latter uses utility theory in addition. In: Mathematical and Statistical Methods in Artificial Intelligence (eds. G. della Riccia, R. Kruse, R. Viertl), Springer Verlag 1995, pp. 171-184

In this paper we are presenting a work concerning the classification and recognition of off-line signatures. Signatures form a special class of handwriting in which legible letters or words may be impossible to exhibit but we can extract some features with the help of some parameters. Our proposed fusion methodology for improving the classification and recognition performance of classifiers is ...

The factors influencing dam safety are complex and their impact on dam safety is fuzzy and uncertain. With the introduction of the Dempster-Shafer theory, BP neural network and Jousselme distance function into dam safety monitoring, a new method for dam safety evaluation is explored. Firstly, the basic probability assignment of the factors influencing dam safety is determined by BP neural netwo...

This paper presents an algorithm for developing models under Dempster-Shafer theory of belief functions for categorical and 'uncertain' logical relationships among binary variables. We illustrate the use of the algorithm by developing belief-function representations of the following categorical relationships: 'AND', 'OR', 'Exclusive OR (EOR)' and 'Not Exclusive OR (NEOR)', and 'AND-NEOR' and of...

There is a problem of applying of combining rules to evidence which were got from different information sources in framework of Dempster-Shafer theory. In this work the conflict measure and index of decreasing of ignorance in frame of Dempster-Shafer theory are introduced for characterization of quality of applied combining rules. Those functionals are analyzed on the bodies of evidences of spe...

Probabilistic Argumentation Systems is an alternative approach for non-monotonic reasoning under uncertainty. It can judge an unknown question in terms of the degree of support and that of possibility about probabilistic entailment. The resulting degree of support and degree of possibility correspond to belief and plausibility, respectively, in the Dempster-Shafer theory of evidence. But the sy...

This paper presents a novel fingerprint classifier fusion algorithm using Dempster-Shafer theory concomitant with update rule. The proposed algorithm accurately matches fingerprint evidences and also efficiently adapts to dynamically evolving database size without compromising accuracy or speed. We experimentally validate our approach using three fingerprint recognition algorithms based on minu...

The research described in this paper addresses issues of designing a computationally effective decision support system which can assist a decision maker in making an optimal choice between several discrete alternatives. A new hybrid approach to multi-attribute decision making under uncertainty incorporating Neural Networks and the Dempster-Shafer theory of evidence is introduced. A neural netwo...