Image Recognition using MLMVN and Frequency Domain Features

Abstract

In this paper, we develop a new approach to image recognition. This approach is based on the analysis of frequency domain features (namely Fourier transform phases corresponding to certain frequencies) using the multilayer neural network with multi-valued neurons (MLMVN). MLMVN is a powerful complex-valued feedforward neural network, which has shown its high efficiency in solving various classification, prediction, and intelligent image filtering problems. As a complex-valued neural network, MLMVN has an ability to treat the phase information properly, completely preserving a circular nature of phase. At the same time it is known that phases contain all information about image edges, their location and spatial orientation. This means that the phase information can be used for image recognition. Particularly, this is the case when it is necessary to recognize objects whose size is known and fixed and it is possible to use phases corresponding to certain frequencies found based on the Nyquist-Shannon theorem as features for recognition. These phases can then be analyzed using MLMVN. We illustrate this approach using the famous MNIST image dataset, which for a 100% recognition rate was achieved. Phases corresponding only to the three lowest frequencies (1 to 3) and just a single hidden layer MLMVN are enough to achieve this result. A batch learning algorithm (MLMVN-SM-LLS) is employed to train the network.