Wavelet-based modeling of singular values for image texture classification

• Autorzy: Ramakrishnan S. , Selvan S.
• Języki publikacji: EN

Abstrakty

EN

A new algorithm based on the wavelet packet transform is proposed for the classification of image textures. Energy matrices are formed from subband coefficients of the wavelet packet transform. Singular value decomposition is then employed on the energy matrices. The probability density function of singular values is modeled as exponential distribution, and the model parameter is estimated using the maximum likelihood estimation technique. The model parameter, one for each subband, is used to form the feature vector. Classification is carried out using the Kullback-Leibler Distance (KLD). Performance of the algorithm is compared with model-based and feature-based methods in terms of the signal-to-noise ratio and the classification rate. Experimental results prove that the proposed algorithm achieves better classification rate under noisy environment.

Słowa kluczowe

EN

wavelet packet transform; image texture classification; singular value decomposition

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2006
• Tom: Vol. 15, No. 2
• Strony: 211--225
• Opis fizyczny: Bibliogr. 17 poz., wykr.

Twórcy

autor

Ramakrishnan S.

autor

Selvan S.

• Department of Information Technology, PSG College of Technology, Coimbatore-641 004, India,

Bibliografia

• [1] Brodatz P.: Textures: A Photographic Album for Artists and Designers. New York: Dover, 1966.
• [2] Chellappa R., Kashyap R. L.: Texture Synthesis Using 2-D Noncausal Autoregressive Models. IEEE Trans. Acoust., Speech, Signal Processing, vol. 33, pp. 194-203, 1985.
• [3] Unser M., Eden M.: Multiresolution Feature Extraction and Selection for Texture Segmentation. IEEE Trans. PAMI, vol. 11, pp. 717-728, 1989.
• [4] Chang T., Kuo C. C. J.: Texture Analysis and Classification with Tree-Structured Wavelet Domain. IEEE Trans. Image Processing, vol. 2, pp. 429-441, 1993.
• [5] Unser M.: Texture Classification and Segmentation Using Wavelet Frames. IEEE Trans, on Image Processing, 4(11), pp. 1549-1560, 1995.
• [6] Valkealahti K., Oja E.: Reduced multidimensional co-occurrence histograms in texture classification. IEEE Trans. On PAMI, voi. 20, no.l, pp. 90-94, 1998.
• [7] Lin-ping Song, Shu-yi Zhang: Singular Value Decomposition-Based Reconstruction Algorithm for Seismic Travel-time Tomography. IEEE Trans, on Image Processing, 8(8), pp. 1152-1154, 1999.
• [8] Bennett J., Khotanzad A.: Maximum Likelihood Estimation Methods for Multispectral Random Field Image Models. IEEE Trans, on PAMI, 21(6), pp. 537-547, 1999.
• [9] Randen T., Husoy J. H.: Filtering for Texture Classification: a Comparative Study. IEEE Trans. PAMI, vol. 21, pp. 291-310, 1999
• [10] Scott T. Acton, Dipti Prasad Mukherjee, Joebob P. Havicek, Alan Conrad Bovik: Oriented Texture Completion By AM-FM Reaction- Diffusion. IEEE Trans, on Image Processing, 10(6), pp. 885-892, 1999.
• [11] Ramakrishna Kakarala, Philip O. Ogunbona : Signal Analysis Using a Multiresolution Form of the Singular Value Decomposition. IEEE Trans. On Image Processing, 10(5), pp. 724-735, 1999.
• [12] Campisi P., Scarano G.: A Multiresolution Approach for Texture Synthesis Using the Circular Harmonic Function. IEEE Trans, on Image Processing, 11( 1), pp. 37-51, 2002.
• [13] Miller D. J., Browning J.: A Mixture Model and EM-Based Algorithm for Class Discovery, Robust Classification, and Outlier Rejection in Mixed Labeled/Unlabeled Data Sets. IEEE Trans, on PAMI, 25(11), pp. 1468-1477, 2003.
• [14] Aujol J., Aubert G., Blanc-Feraud L.: Wavelet-Based Level Set Evolution for Classification of Textured Images. IEEE Trans, on Image Processing, 12(12), pp.1634-1641, 2003.
• [15] Guoliang Fan, Xiang-Gen Xia: Wavelet-Based Texture Analysis and Synthesis Using Hidden Markov Models. IEEE Trans, on Circuits and Systems I: Fundamental Theory and Applications, 50(1), pp.106-120, 2003.
• [16] Truong T. Nguyen, Soontorn Oraintara: Multiresolution Direction Filterbanks: Theory, Design, and Applications. IEEE Trans, on Signal Processing, 53(10), pp. 3895-3905, 2005.
• [17] Clausi D. A., Huang Deng: Design-Based Texture Feature Fusion Using Gabor Filters and Co-Occurrence Probabilities. IEEE Trans, on Image Processing, 14(7), pp. 925-936, 2005.