Wood defects classification using Artificial Metaplasticity neural network

Artificial Metaplasticity (AMP) is a novel Artificial Neural Network (ANN) training algorithm inspired in biological metaplasticity property of neurons and Shannon´s information theory. During training phase, the AMP training algorithm gives more relevance to the less frequent patterns and subtracts relevance to the frequent ones, achieving a much more efficient training, while at least maintaining the MLP´s performance. AMP is specially recommended when few patterns are available to train the network. In this paper, we implement an Artificial Metaplasticity MLP (AMMLP) in order to classify defects in wood images. The defects are three different types of knots found in wood surfaces. Classification is based on the features obtained from Gabor filters. Experimental results show that AMMLPs reach better accuracy than the classical BP algorithm as well as with recently proposed algorithms applied on the same database.