Optimized multichannel decomposition for texture segmentation using Gabor filter bank

Abstract

Texture segmentation and analysis is an important aspect of pattern recognition and digital image processing. Previous approaches to texture analysis and segmentation perform multi-channel filtering by applying a set of filters to the image. In this paper we describe a texture segmentation algorithm based on multi-channel filtering that is optimized using diagonal high frequency residual. Gabor band pass filters with different radial spatial frequencies and different orientations have optimum resolution in time and frequency domain. The image is decomposed by a set of Gabor filters into a number of filtered images; each one contains variation of intensity on a sub-band frequency and orientation. The features extracted by Gabor filters have been applied for image segmentation and analysis. There are some important considerations about filter parameters and filter bank coverage in frequency domain. This filter bank does not completely cover the corners of the frequency domain along the diagonals. In our method we optimize the spatial implementation for the Gabor filter bank considering the diagonal high frequency residual. Segmentation is accomplished by a feedforward backpropagation multi-layer perceptron that is trained by optimized extracted features. After MLP is trained the input image is segmented and each pixel is assigned to the proper class.