Texture segmentation using Gabor filter and multi-layer perceptron

Previous approaches to texture analysis and segmentation perform multi-channel filtering by applying a set of filters in frequency domain or a set of masks in spatial domain. In this paper we describe a texture segmentation algorithm based on multi-channel filtering in conjunction with neural networks for feature extraction and segmentation. The features extracted by Gabor filters have been applied for image segmentation and analysis. There are some important considerations about filter parameters and the reduction of feature dimensions. Here we introduce a method to extract optimal feature dimensions using competitive networks and multilayer perceptrons. We present the segmentation results using different bandwidths. The comparison of segmentation results generated using our method and previous research using learning vector quantization (LVQ) is presented.