Discriminant eigenfeatures-based image recognition implemented on a photorefractive correlator

Abstract

In this paper, we incorporate the theory of the multidimensional discriminant analysis into a photorefractive correlator architecture. In this approach, a set of eigenimages extracted from a large number of training images by K-L transform are stored in a photorefractive crystal by using the two-wave mixing volume holographic storage technique and used as the reference images in the photorefractive correlator. When any new image inputs the correlator, angularly separated beams with different light intensities are obtained simultaneously. They represent the optical correlation results between the input and the set of eigenimages and can be regarded as eigenfeatures. Then the multidimensional discriminant analysis will be applied to these features for training and classification. During both processes, a bifurcating tree structure is used, by which the recognition speed of the system can be greatly improved. This approach takes the advantages of both the high degree of parallelism of the photorefractive correlator and the optimal discriminating ability of the multivariate statistical methods for classification.