Concept recognition: An approximate reasoning framework

Abstract

This article presents a knowledge based methodology for recognizing concept instances in complex data such as natural scenes or speech signals. the architecture of a prototype system performing this task is also described. In order to obtain the capability of handling noisy patterns, the knowledge representation is based on continuous valued logics, and the inference engine is able to do sophisticated reasoning about the knowledge it uses in order to take into account the possible degradation of cues in the pattern. The knowledge base is subdivided into a bulk knowledge and a degradation theory. the bulk knowledge consists of production rules capturing discriminating cues in the signal as they appear in the normal cases. the degradation theory describes how the cues can be degraded owing to insertion and deletion errors. The whole classification process consists of two phases. First of all, the rules of the bulk knowledge are applied to the pattern, trying to obtain a suitable classification, and the evidence assigned to the rules is combined with a technique based on Dempster-Shafer theory. In the second phase, the classification is refined taking into account also the possible degradations in the pattern. the resulting system is then able to deal with different kinds of uncertainty and errors, such as fluctuation in the values measured for the pattern and insertion-deletion errors. the method is illustrated and evaluated on a simple example of spoken word recognition. (This work has been developed within the pilot project ESPRIT No. 26. © 1989 Wiley Periodicals, Inc.)