A method of organization of a parallel-hierarchical network for image recognition

Abstract

A method of parallel-hierarchical transformation based on population coding is described and the use of this method for image recognition is considered. This parallel-hierarchical transformation is described as a system model for image recognition. Theoretical information, experimental investigations, and a software realization are presented.

Modeling of a method of parallel hierarchical transformation for fast recognition of dynamic images

“…In the conducted experiments, an average portion of good route fragments was 17%, as compared to 50% in [21]. However, after the training, it increased to 79.25% (as compared to 65% [21]).…”

“…To normalize results of the PH network with tuning coefficients (2), the main property of the PH network will be used: [21]. Then, a right part of the normalizing Equation (3) represents a ratio of the sum of products of averaged tuning coefficients and tail elements to the sum of the tail elements of the network.…”

“…After that, G-transformation of information [21] takes place at all levels of the PH network. The Gtransformation is performed in the following way.…”

“…A vector of incoming image is sent to the PH network input (to input elements of first-level branches of the network, Figure 1). A mathematical model of the parallel decomposition of the set μ ¼ a i f g; i ¼ 1; n [21], used in each branch of the PH network, looks as follows:…”

“…In particular, to perform a real-time classification and analysis of, for instance, adjacent frames of a multimedia image, the use of the normalizing equation gives better results in comparison to other approaches [21].…”