Neural network-based segmentation of textures using Gabor features

Abstract

The effectiveness of Gabor filters for texture segmentation is well known. In this paper, we propose a texture identification scheme, based on a neural network (NN) using Gabor features. The features are derived from both the Gabor cosine and sine filters. Through experiments, we demonstrate the effectiveness of a NN based classifier using Gabor features for identifying textures in a controlled environment. The neural network used for texture identification is based on the multilayer perceptron (MLP) architecture. The classification results obtained show an improvement over those obtained by K-means clustering and maximum likelihood approaches.