Texture classification using logical operators

In this paper, a new algorithm for texture classification based on logical operators is presented. Operators constructed from logical building blocks are convolved with texture images. An optimal set of six operators are selected based on their texture discrimination ability. The responses are then converted to standard deviation matrices computed over a sliding window. Zonal sampling features are computed from these matrices. A feature selection process is applied and the new set of features are used for texture classification. Classification of several natural and synthetic texture images are presented demonstrating the excellent performance of the logical operator method. The computational superiority and classification accuracy of the algorithm is demonstrated by comparison with other popular methods. Experiments with different classifiers and feature normalization are also presented. The Euclidean distance classifier is found to perform best with this algorithm. The algorithm involves only convolutions and simple arithmetic in the various stages which allows faster implementations. The algorithm is applicable to different types of classification problems which is demonstrated by segmentation of remote sensing images, compressed and reconstructed images and industrial images.

[1]  B. J. Falkowski,et al.  Properties and ways of calculation of multi-polarity generalized Walsh transforms , 1994 .

[2]  Chip-Hong Chang,et al.  Generation of multi-polarity arithmetic transform from reduced representation of Boolean functions , 1995, Proceedings of ISCAS'95 - International Symposium on Circuits and Systems.

[3]  Joyce Van de Vegte,et al.  Fundamentals of Digital Signal Processing , 2001 .

[4]  Marek Perkowski,et al.  Algorithm and architecture for Gray code ordered fast Walsh transform , 1990, IEEE International Symposium on Circuits and Systems.

[5]  Glenn Healey,et al.  An analytical and experimental study of the performance of Markov random fields applied to textured images using small samples , 1996, IEEE Trans. Image Process..

[6]  C.-C. Jay Kuo,et al.  Texture analysis and classification with tree-structured wavelet transform , 1993, IEEE Trans. Image Process..

[7]  T. Lonnestad.  A new set of texture features based on the Haar transform , 1992, Proceedings., 11th IAPR International Conference on Pattern Recognition. Vol. III. Conference C: Image, Speech and Signal Analysis,.

[8]  Terry Caelli,et al.  An adaptive computational model for texture segmentation , 1988, IEEE Trans. Syst. Man Cybern..

[9]  Richard W. Conners,et al.  A Theoretical Comparison of Texture Algorithms , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Ahmed H. Tewfik,et al.  A binary wavelet decomposition of binary images , 1996, IEEE Trans. Image Process..

[11]  Bedrich J. Hosticka,et al.  A comparison of texture feature extraction using adaptive gabor filtering, pyramidal and tree structured wavelet transforms , 1996, Pattern Recognit..

[12]  Andrzej Drygajlo.  Butterfly orthogonal structure for fast transforms, filter banks and wavelets , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[13]  Phil Brodatz,et al.  Textures: A Photographic Album for Artists and Designers , 1966 .

[14]  Ezzatollah Salari,et al.  Texture segmentation using hierarchical wavelet decomposition , 1995, Pattern Recognit..

[15]  Vidya B. Manian,et al.  Scaled and rotated texture classification using a class of basis functions , 1998, Pattern Recognit..

[16]  Keinosuke Fukunaga,et al.  Effects of Sample Size in Classifier Design , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Marek Perkowski,et al.  A fast computer implementation of adding and arithmetic multi-polarity transforms for logic design , 1991, [1991] Proceedings of the 34th Midwest Symposium on Circuits and Systems.

[18]  Chun-Shien Lu,et al.  Unsupervised texture segmentation via wavelet transform , 1997, Pattern Recognit..

[19]  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..

[20]  Z. Ling,et al.  Texture segmentation using hierarchical wavelet decomposition , 1995, 1995 Proceedings of the IEEE International Symposium on Industrial Electronics.

[21]  Susanto Rahardja,et al.  Fast transforms for orthogonal logic , 1995, Proceedings of ISCAS'95 - International Symposium on Circuits and Systems.

[22]  J.-L. Wu,et al.  Feature extraction capability of some discrete transforms , 1991, 1991., IEEE International Sympoisum on Circuits and Systems.

[23]  Lonnie C. Ludeman,et al.  Fundamentals of Digital Signal Processing , 1986 .

[24]  Marek Perkowski,et al.  A family of all essential Radix-2 addition/subtraction multi-polarity transforms: algorithms and interpretations in Boolean domain , 1990, IEEE International Symposium on Circuits and Systems.

[25]  C.-C. Jay Kuo,et al.  A wavelet transform approach to texture analysis , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[26]  Ephraim Feig,et al.  Hadamard transforms on multiply/add architectures , 1994, IEEE Trans. Signal Process..

[27]  Jane You,et al.  Classification and segmentation of rotated and scaled textured images using texture "tuned" masks , 1993, Pattern Recognit..

[28]  Rama Chellappa,et al.  Multiresolution Gauss-Markov random field models for texture segmentation , 1997, IEEE Trans. Image Process..

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

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

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

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

[33]  A. R. Schaefer,et al.  Large area megapixel CCD arrays , 1990, Proceedings of the 33rd Midwest Symposium on Circuits and Systems.