Classification of color textures by Gabor filtering

A novel approach to Gabor filtering of color textures is introduced. It is based on the complex chromatic Fourier transform. Complex colors are derived from the HSL color space representing intensity- independent color textures. Additionally, a novel Gabor texture feature for the grayscale as well as the color domain is proposed. It relies on local phase changes characterizing the homogeneity of a texture in the spatial frequency domain. Several classification experiments on two image databases are performed to study the texture features according to different color spaces and Gabor filter bank variants. The color features show significantly better results than the grayscale features. Although they are completely intensity-independent, the features on the basis of the complex color space show satisfying results. The RGB based features, where color and intensity work inherently together, perform best. Especially the local phase change measure supplements the known amplitude measure appropriately.

[1]  Dennis Gabor,et al.  Theory of communication , 1946 .

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

[3]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[4]  F. Harris.  On the use of windows for harmonic analysis with the discrete Fourier transform , 1978, Proceedings of the IEEE.

[5]  H. Derin,et al.  Segmentation of textured images using Gibbs random fields , 1986 .

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

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

[8]  C.-C. Jay Kuo,et al.  Tree-structured wavelet transform for textured image segmentation , 1992, Optics & Photonics.

[9]  Arthur Robert Weeks,et al.  RGB color enhancement using homomorphic filtering , 1995, Electronic Imaging.

[10]  Stephen J. Sangwine,et al.  Colour object location using complex coding in the frequency domain , 1995 .

[11]  S. Sangwine.  Fourier transforms of colour images using quaternion or hypercomplex, numbers , 1996 .

[12]  Stephen J. Sangwine,et al.  The discrete quaternion Fourier transform , 1997 .

[13]  Tieniu Tan,et al.  Rotation Invariant Texture Features from Gabor Filters , 1998, ACCV.

[14]  Jitendra Malik,et al.  Color- and texture-based image segmentation using EM and its application to content-based image retrieval , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[15]  Amit Jain,et al.  A multiscale representation including opponent color features for texture recognition , 1998, IEEE Trans. Image Process..

[16]  Paul Scheunders,et al.  Wavelet correlation signatures for color texture characterization , 1999, Pattern Recognit..

[17]  Markku Hauta-Kasari,et al.  Multi-spectral Texture Segmentation Based on the Spectral Cooccurrence Matrix , 1999, Pattern Analysis & Applications.

[18]  Trygve Randen,et al.  Filtering for Texture Classification: A Comparative Study , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  B. S. Manjunath,et al.  Rotation-invariant texture classification using a complete space-frequency model , 1999, IEEE Trans. Image Process..

[20]  Christoph Palm,et al.  Gabor Filtering of Complex Hue/Saturation Images for Color Texture Classification , 2000 .

[21]  Alok Gupta,et al.  Color and texture fusion: application to aerial image segmentation and GIS updating , 2000, Image Vis. Comput..

[22]  Stephen J. Sangwine,et al.  Colour in image processing , 2000 .

[23]  Fabrizio Smeraldi,et al.  Saccadic search with Gabor features applied to eye detection and real-time head tracking , 2000, Image Vis. Comput..

[24]  T Caelli,et al.  Theory of spatiochromatic image encoding and feature extraction. , 2000, Journal of the Optical Society of America. A, Optics, image science, and vision.

[25]  K. Plataniotis,et al.  Color Image Processing and Applications , 2000 .

[26]  Juan Campos,et al.  Colour information as a third dimension in Fourier transform and correlation , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[27]  Soo-Chang Pei,et al.  Efficient implementation of quaternion Fourier transform, convolution, and correlation by 2-D complex FFT , 2001, IEEE Trans. Signal Process..

[28]  Jussi Parkkinen,et al.  Color features for quality control in ceramic tile industry , 2001 .