Using Gabor Filters as Image Multiplier for Tropical Wood Species Recognition System

Abstract

One of the main problems in wood species recognition systems is the lack of discriminative features of the texture images. In order to overcome this, we use Gabor filter in the pre-processing stage of the wood texture image to multiply the number of features for a single image, thus providing more information for feature extractor to capture. The textural wood features are extracted using two feature extraction methods which are co-occurrence matrix approach, known as grey level co-occurrence matrix (GLCM) and also Gabor filters to generate more variation of features and to improve the accuracy rate. The combined features extracted from GLCM and Gabor filters are sent to the classifier module. A multi-layer neural network based on the popular back propagation (MLBP) algorithm is used for classification. The results show that increasing the number of features by using Gabor filters as image multiplier and the combination of features from Gabor filters and GLCM feature extractors improved the accuracy rate of the wood species recognition system.