Finding the number of hidden neurons for an MLP neural network using coarse to fine search technique

Abstract

Skin detection is an important preliminary process for subsequent feature extraction in image processing techniques. There are several techniques that are used for skin detection. In this work, the multi-layer perceptron (MLP) neural network is used. One of the important aspects of MLP is how to determine the network topology. The number of neurons in the inputs and output layers are determined by the number of available inputs and required outputs respectively. Thus, the only thing remaining is how to determine the number of neurons in the hidden layer. Therefore, we employed the coarse to fine search method to find the number of neurons. First, the number of hidden neurons is initially set using the binary search mode, HN=1, 2, 4, 8, 16, 32, 64 and 128, where HN indicates the number of hidden neurons. The 30 networks with these HN values are trained and their Mean Squared Error (MSE) is calculated. Then a sequential search, fine search, will be used in the neighbourhood of the HN that gave the lowest MSE. The selected number of neurons in the hidden layer is the lowest HN that gave the lowest MSE. The YC <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> C <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">r</sub> colour space is used in this work due to its capability to separate the luminance and chrominance components explicitly. Several chrominance components are investigated.