title: A hybrid learning network for shift-invariant recognition.
abstract: A neural network and the associated learning algorithm are presented as a generic approach for invariant recognition of visual patterns independent of their geometric attributes, such as spatial location, orientation and scale. The network is a multi-layer hierarchy with each layer composed of a set of groups of nodes. The groups of the input layer represent local areas spatially arranged in the visual field according to the geometric variations. Each node in the subsequent higher layers receives input laterally from other groups of the same layer as well as vertically from the layer below. The learning that takes place in the vertical feed forward paths between layers is based on an unsupervised hybrid algorithm combining both competitive learning and Hebbian learning. As the result of the architecture and the hybrid learning, the desired invariant recognition emerges at the output layer of the network. The network can serve as a simple and biologically plausible computational model to account for the invariant object recognition in the biological visual system. Also, as the algorithm is generic and robust, it can be applied to solve various practical recognition problems.
authors.affiliation: Engineering Department, Harvey Mudd College, Claremont, CA 91711, USA. ruye_wang@hmc.edu
authors.forename: R
authors.initials: R
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D006801:Humans; D007091:Image Processing, Computer-Assisted; D016014:Linear Models; D009415:Nerve Net / Q000502:physiology; D016571:Neural Networks, Computer; D009434:Neural Pathways / Q000502:physiology; D009474:Neurons / Q000502:physiology; D017711:Nonlinear Dynamics; D010364:Pattern Recognition, Visual / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology; D014793:Visual Cortex / Q000502:physiology