Binary Factorization

Computing similarity measure between images is a basic task required for image classification and matching. The technique proposed explores the Moire phenomenon for determining the similarity measure for two images. When two similar images are superimposed the concentric Moire rings point to the location of the fixed point. A Hough based technique is used for circle detection. The algorithm is successfully applied to the face recognition problem.

The unsupervised learning of feature extraction in high dimensional patterns is a central problem for neural network approach. Feature extraction is the procedure which maps original patterns into the features (or factors) space of reduced dimension. In this paper we demonstrate that Hebbian learning in Hopfield-like neural network is a natural procedure for unsupervised learning of feature extraction. Due to this learning, factors become the attractors of network dynamics, hence they can be revealed by the random search. The neurodynamics is analyzed by Single-Step approximation, which is known [1] to be rather accurate for sparsely encoded Hopfield network. Thus, the analysis is restricted by the case of sparsely encoded factors. The accuracy of Single-Step approximation is confirmed by computer simulations.

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