1-D Directional Filter Based Texture Descriptor in Fractional Fourier Domain

Kai Tian¹, Hongzhang Jin¹,Liying Zheng²

(1. College of Automation, Harbin Engineering University, Harbin 150001, China;2. College of Computer Science and Technology, Harbin Engineering University, Harbin 150001, China)



Abstract:

Texture analysis is a fundamental field in computer vision. However, it is also a particularly difficult problem for no universal mathematical model of real world textures. By extending a new application of the fractional Fourier transform (FrFT) in the field of texture analysis, this paper proposes an FrFT-based method for describing textures. Firstly, based on the Radon-Wigner transform, 1-D directional FrFT filters are designed to two types of texture features, i.e., the coarseness and directionality. Then, the frequencies with maximum and median amplitudes of the FrFT of the input signal are regarded as the output of the 1-D directional FrFT filter. Finally, the mean and the standard deviation are used to compose of the feature vector. Compared to the WD-based method, three benefits can be achieved with the proposed FrFT-based method, i.e., less memory size, lower computational load, and less disturbed by the cross-terms. The proposed method has been tested on 16 standard texture images. The experimental results show that the proposed method is superior to the popular Gabor filtering-based method.

Key words:  fractional Fourier transform  texture analysis  Radon-Wigner transform  1-D directional window

DOI：10.11916/j.issn.1005-9113.2015.05.019

Clc Number:TP391.4

Fund: