Hybrid Associative Memories And Metric Data Models

Lev Goldfarb; Raj Verma

doi:10.1117/12.976606

22 August 1988 Hybrid Associative Memories And Metric Data Models

Lev Goldfarb, Raj Verma

Proceedings Volume 0938, Digital and Optical Shape Representation and Pattern Recognition; (1988) https://doi.org/10.1117/12.976606
Event: 1988 Technical Symposium on Optics, Electro-Optics, and Sensors, 1988, Orlando, FL, United States

Abstract

An approach to the design of associative memories and pattern recognition systems which utilizes efficiently hybrid architectures is illustrated. By associative memory we mean a database organization that supports retrieval by content and not only by name (or address), as is the case with practically all existing database systems. The approach is based on a general, metric, model for pattern recognition which was developed to unify in a single model two basic approaches to pattern recognition-geometric and structural-preserving the advantages of each one. The metric model offers the designer a complete freedom in the choice of both the object representation and the dissimilarity measure, and at the same time provides a single analytical framework for combining several object representations in a very efficient recognition scheme. It is our fervent hope that the paper will attract researchers interested in the development of associative memories or image recognition systems to experiment with various optical dissimilarity measures (between two images) the need for which becomes so acute with the realization of the possibilities offered by the metric model.

Citation Download Citation

Lev Goldfarb and Raj Verma "Hybrid Associative Memories And Metric Data Models", Proc. SPIE 0938, Digital and Optical Shape Representation and Pattern Recognition, (22 August 1988); https://doi.org/10.1117/12.976606

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