Multiple elastic modules for visual pattern recognition

Abstract

Fast synaptic plasticity, used to associate topologically ordered features in an input image to those of previously learned objects, has been previously proposed as a possible model for object recognition (von der Malsburg & Bienenstock, 1987, Europhysics Letters, 3(11), 1243–1249). In this paper, it is argued that in addition to rapid link dynamics, fast receptive field size dynamics are necessary to automatically escape from poor local matches and also allow for simultaneous recognition of multiple objects. Furthermore, a feature locking mechanism with a properly designed hysteresis property is needed to handle complex, cluttered, and dynamic scenes. The multiple elastic modules (MEM) model, described in this paper, utilizes newly developed dynamics that locate and recognize a previously learned object based on expected spatial arrangement of local features. The MEM model can be viewed as using a deformable template of an object to search the input scene. Unlike many of the current artificial neural network models, the proposed MEM model attempts to capture many of the functions available in the biological visual system by providing mechanisms for: multi-model feature integration, generation and maintenance of focus of attention, multiresolution hierarchical searching, and top-down expectation driven processing coupled with bottom-up feature activation processing. In addition, the MEM dynamics, unlike similar template matching approaches (Konen et al., 1994, Neural Networks, 7(6/7), 1019–1030; Yuille et al., 1992, International Journal of Computer Vision, 8(2), 99–111), does not converge to false objects when there are no sufficiently familiar objects in the scene. The performance of the MEM model in detection and recognition of objects through a number of computer simulations is demonstrated.