Texture classification using windowed Fourier filters

Author

Azencott, Robert ; Wang, Jia-Ping ; Younes, Laurent

Author_Institution

Centre de Math. et Leurs Applications, Ecole Normale Superieure de Cachan, France

Volume

19

Issue

2

fYear

1997

fDate

2/1/1997 12:00:00 AM

Firstpage

148

Lastpage

153

Abstract

We define a distance between textures for texture classification from texture features based on windowed Fourier filters. The definition of the distance relies on an interpretation of our texture attributes in terms of spectral density when the texture can be considered as a Gaussian random field. The distance between textures is then defined as a symmetrized Kullback distance which is a simple function of the attributes and does not require any normalization. An experimental analysis using Gabor filters, and in particular a comparison to quadratic distances, shows the efficiency and robustness of the method

Keywords

Fourier transform spectra; Gaussian processes; computer vision; filtering theory; image classification; image segmentation; image texture; spectral analysis; Gabor filters; Gaussian random field; computer vision; quadratic distances; segmentation; spectral density; symmetrized Kullback distance; texture attributes; texture classification; windowed Fourier filters; Application software; Computer vision; Data mining; Decorrelation; Design methodology; Gabor filters; Image segmentation; Moment methods; Robustness; Statistics;

fLanguage

English

Journal_Title

Pattern Analysis and Machine Intelligence, IEEE Transactions on

Publisher

ieee

ISSN

0162-8828

Type

jour

DOI

10.1109/34.574796

Filename

574796