Similarity retrieval from image databases: neural networks can deliver

Richard M. Rickman; T. John Stonham

doi:10.1117/12.143658

Abstract

We address some of the problems of accessing database images which do not contain any indexing information and investigate methods of automating search strategies which currently rely on human operators to match the target against a number of images in the database. Such problems might include the extraction of facial photographs from a library given a suspect or the registration of new trademarks whose uniqueness must be assured. The object of the retrieval mechanism is to narrow down the search space for final perusal by the human operator. We present a neural network based coding scheme to retrieve images from a database according to the degree of similarity with a target image. The code represents each image with respect to a set of feature archetypes learned by the neural network during a training phase. We introduce a novel neural network learning law which performs an extremely efficient implementation of principal component analysis and maximizes the amount of information conveyed by the code. We present results using a database of machine printed fonts and discuss how the image size, the database diversity, and code length affect the efficacy of the retrieval mechanism.

Citation Download Citation

Richard M. Rickman and T. John Stonham "Similarity retrieval from image databases: neural networks can deliver", Proc. SPIE 1908, Storage and Retrieval for Image and Video Databases, (14 April 1993); doi: 10.1117/12.143658; https://doi.org/10.1117/12.143658

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