Image Recognition on the Neural Network Based on Multi-Valued Neurons

Multi-valued neurons are the neural processing elements with complex-valued weights, huge functionality (it is possible to implement on the single neuron arbitrary mapping described by partially defined multiple-valued function), quickly converged learning algorithms. Such features of the multi-valued neurons may be used for solution of the different kinds of problems. Neural network with multi-valued neurons for image recognition will be considered in the paper. Such a network with original architecture analyzes the phases of the Fourier spectral coefficients corresponding to the low frequencies. Quickly converged learning algorithm and huge functionality of multi-valued neurons allow to get 100% successful recognition of the different classes of images including the blurred and corrupted ones. Simulation results are presented on the example of face recognition.

• Type: Conference
• Year: 2000
• Where: ICPR
• Authors: Igor N. Aizenberg, Naum N. Aizenberg, Constantine Butakoff, Elya Farberov
• Keywords: Computer Vision, Converged Learning Algorithm, Huge Functionality, ICPR 2000, Multi-valued Neurons