Rotation invariant texture classification by ridgelet transform and frequency-orientation space decomposition

A new rotation invariant feature extraction method in the ridgelet transform domain for texture classification is proposed. Ridgelet transform can be divided into two stages: the Radon transform stage and the 1-D wavelet transform stage. According to the Projection-Slice theorem, the Radon transform actually provides information about the image data on a polar-grid in the frequency domain. This is ideal for rotation invariant feature extraction. Furthermore, by using wavelets that have compact support in the frequency domain, we can actually use ridgelet transform to achieve frequency-orientation decompositions for the given image data, which is similar to the multi-channel filtering technique. This makes the proposed method very effective in capturing texture properties for classification. Experimental results show a good performance achieved by the proposed method.

[1]  Donald A. Adjeroh,et al.  Efficient texture analysis of SAR imagery , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Dimitrios Charalampidis,et al.  Wavelet-based rotational invariant roughness features for texture classification and segmentation , 2002, IEEE Trans. Image Process..

[3]  E. Candès,et al.  Ridgelets: a key to higher-dimensional intermittency? , 1999, Philosophical Transactions of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[4]  Anil K. Jain,et al.  Texture classification and segmentation using multiresolution simultaneous autoregressive models , 1992, Pattern Recognit..

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

[6]  Dragutin Petkovic,et al.  Query by Image and Video Content: The QBIC System , 1995, Computer.

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

[8]  Hamid Soltanian-Zadeh,et al.  Radon transform orientation estimation for rotation invariant texture analysis , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  U Ranft,et al.  Random field models in the textural analysis of ultrasonic images of the liver , 1996, IEEE Trans. Medical Imaging.

[10]  Arivazhagan Selvaraj,et al.  Texture classification using ridgelet transform , 2005, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05).

[11]  Minh N. Do,et al.  Rotation invariant texture characterization and retrieval using steerable wavelet-domain hidden Markov models , 2002, IEEE Trans. Multim..

[12]  R. Porter,et al.  Robust rotation-invariant texture classification: wavelet, Gabor filter and GMRF based schemes , 1997 .

[13]  Tieniu Tan,et al.  Brief review of invariant texture analysis methods , 2002, Pattern Recognit..

[14]  Ke Huang,et al.  Rotation Invariant Texture Classification with Ridgelet Transform and Fourier Transform , 2006, 2006 International Conference on Image Processing.

[15]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[16]  Tieniu Tan,et al.  Extraction of noise robust rotation invariant texture features via multichannel filtering , 1997, Proceedings of International Conference on Image Processing.

[17]  Sridha Sridharan,et al.  Texture for script identification , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Chi-Man Pun,et al.  Log-Polar Wavelet Energy Signatures for Rotation and Scale Invariant Texture Classification , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  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..

[20]  Gabor T. Herman,et al.  Image reconstruction from projections : the fundamentals of computerized tomography , 1980 .

[21]  Alessandro Neri,et al.  Robust rotation-invariant texture classification using a model based approach , 2004, IEEE Transactions on Image Processing.

[22]  Anthonio Teolis,et al.  Computational signal processing with wavelets , 1998, Applied and numerical harmonic analysis.

[23]  Tieniu Tan,et al.  Rotation Invariant Texture Features and Their Use in Automatic Script Identification , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  S Marcelja,et al.  Mathematical description of the responses of simple cortical cells. , 1980, Journal of the Optical Society of America.

[25]  Ching Y. Suen,et al.  Rotation-Invariant Texture Classification Using Steerable Gabor Filter Bank , 2005, ICIAR.

[26]  Sargur N. Srihari,et al.  Document Image Binarization Based on Texture Features , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[27]  Adam Krzyzak,et al.  Rotation invariant pattern recognition using ridgelets, wavelet cycle-spinning and Fourier features , 2005, Pattern Recognit..

[28]  S. Arivazhagan,et al.  Texture classification using ridgelet transform , 2005 .

[29]  Robert M. Haralick,et al.  Zone classification using texture features , 1996, Proceedings of 13th International Conference on Pattern Recognition.

[30]  N. Kingsbury.  Complex Wavelets for Shift Invariant Analysis and Filtering of Signals , 2001 .

[31]  Hamid Soltanian-Zadeh,et al.  Rotation-invariant multiresolution texture analysis using Radon and wavelet transforms , 2005, IEEE Transactions on Image Processing.

[32]  Adam Krzyzak,et al.  Rotation invariant feature extraction using Ridgelet and Fourier transforms , 2006, Pattern Analysis and Applications.

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