Texture Classification Using Windowed Fourier Filters

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.

[1]  Rama Chellappa,et al.  Classification of textures using Gaussian Markov random fields , 1985, IEEE Trans. Acoust. Speech Signal Process..

[2]  André Gagalowicz,et al.  Blind texture segmentation , 1988, [1988 Proceedings] 9th International Conference on Pattern Recognition.

[3]  Jian Fan,et al.  Texture Classification by Wavelet Packet Signatures , 1993, MVA.

[4]  Mary M. Galloway,et al.  Texture analysis using gray level run lengths , 1974 .

[5]  Anil K. Jain,et al.  Markov Random Field Texture Models , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Olivier D. Faugeras,et al.  Decorrelation Methods of Texture Feature Extraction , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Jia-Lin Chen,et al.  Automatic unsupervised texture segmentation using hidden Markov model , 1993, 1993 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[8]  C. H. Chen,et al.  A Study of Texture Classification Using Spectral Features , 1982 .

[9]  Rama Chellappa,et al.  Estimation and choice of neighbors in spatial-interaction models of images , 1983, IEEE Trans. Inf. Theory.

[10]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[11]  Azriel Rosenfeld,et al.  A Comparative Study of Texture Measures for Terrain Classification , 1975, IEEE Transactions on Systems, Man, and Cybernetics.

[12]  Peter Guttorp,et al.  Series of Irregular Observations: Forecasting and Model Building (Robert Azencott and Didier Dacunha-Castelle) , 1988 .

[13]  Rangasami L. Kashyap,et al.  A Model-Based Method for Rotation Invariant Texture Classification , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Robert Azencott,et al.  Series of Irregular Observations , 1986 .

[15]  J. Besag.  On the Statistical Analysis of Dirty Pictures , 1986 .

[16]  John G. Daugman,et al.  Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression , 1988, IEEE Trans. Acoust. Speech Signal Process..

[17]  M. Rosenblatt.  Stationary sequences and random fields , 1985 .

[18]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[19]  Wilson S. Geisler,et al.  Multichannel Texture Analysis Using Localized Spatial Filters , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[20]  Donald Geman,et al.  Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  F. S. Cohen,et al.  Classification of Rotated and Scaled Textured Images Using Gaussian Markov Random Field Models , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  M.,et al.  Statistical and Structural Approaches to Texture , 2022 .

[23]  Yehoshua Y. Zeevi,et al.  The Generalized Gabor Scheme of Image Representation in Biological and Machine Vision , 1988, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Rama Chellappa,et al.  Texture synthesis and compression using Gaussian-Markov random field models , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[25]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.