Image decomposition and texture segmentation via sparse representation

IEEE Signal Processing Letters

Volumn 15, Issue , 2008, Pages 641-644

  Zhang, Fan ^a   Ye, Xiaoqiong ^a   Liu, Wenyu ^a  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

Author keywords

Clustering; Principal component analysis (PCA); Sparse representation; Texture; Total variation; Unsupervised learning

Indexed keywords

CLUSTERING; IMAGE DECOMPOSITION; PRINCIPAL COMPONENT ANALYSIS (PCA); SELF-SIMILAR; SPARSE REPRESENTATION; TEXTURE SEGMENTATION; TOTAL VARIATION;

DECOMPOSITION; TEXTURES; UNSUPERVISED LEARNING;

PRINCIPAL COMPONENT ANALYSIS;

EID: 67650180057     PISSN: 10709908     EISSN: None     Source Type: Journal    
DOI: 10.1109/LSP.2008.2002722     Document Type: Article

References (17)

1. F. Zhang, W. Y. Liu, and C. X. Liu, "High capacity watermarking in nonedge texture under statistical distortion constraint," in Asian Conf. Computer Vision, Nov. 2007, vol. 4843, LNCS, pp. 282-291.
2. D. Mumford and J. Shah, "Optimal approximations by piecewise smoothed functions and associated variational problems," Commun. Pure Appl. Math., vol. XLII, pp. 577-685, 1989.
3. L. Rudin, S. Osher, and E. Fatemi, "Nonlinear total variation based noise removal algorithms," Physica D, vol. 60, pp. 259-268, 1992.
4. S. Alliney, "Digital filters as absolute norm regularizers," IEEE Trans. Signal Process., vol. 40, no. 6, pp. 1548-1562, Jun. 1992.
5. Y. Meyer, "Oscillating patterns in image processing and non linear evolution equations," in University Lecture Series. Providence, RI: AMS, 2002, vol. 22.
6. J. F. Aujol, G. Gilboa, T. Chan, and S. Osher, "Structure-texture image decomposition - Modeling, algorithms, and parameter selection," Int. J. Comput. Vis., vol. 67, pp. 111-136, 2006.
7. J.-L. Starck, M. Elad, and D. Donoho, "Image decomposition via the combination of sparse representations and a variational approach," IEEE Trans. Image Process., vol. 14, no. 10, pp. 1570-1582, Oct. 2005.
8. G. Peyré, J. Fadili, and J.-L. Starck, "Learning adapted dictionaries for geometry and texture separation," in Proc. SPIE, Sep. 2007, vol. 6701, 67011T.
9. C. Guo, S. C. Zhu, and Y. N. Wu, "Primal sketch: Integrating texture and structure," Comput. Vis. Image Understand., vol. 106, pp. 5-19, 2005.
10. M. Aharon, M. Elad, and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Trans. Signal Process., vol. 54, no. 11, pp. 4311-4322, Nov. 2006.
11. M. Tipping and C. Bishop, "Mixtures of probabilistic principal component analyzers," Neural Comput., vol. 11, pp. 443-482, 1999.
12. J. Li et al., "Kernel principal component analysis in pixels clustering," in Proc. IEEE Int. Conf. Web Intelligence, 2005, pp. 786-789.
13. I. Nabney and C. Bishop, Netlab Neural Network Software, 2003. [Online]. Available: http://www.ncrg.aston.ac.uk/netlab.
14. D. Goldfarb and W. Yin, Parametric Maximum Flow Algorithms for Fast Total Variation Minimization, Rice CAAM, 2007, TR07-09.
15. J. A. Tropp, "Topics in sparse approximation," Ph.D. dissertation, Univ. Texas, Austin, 2004.
16. D. de Ridder, J. Kittler, and R. P. W. Duin, "Probabilistic PCA and ICA subspace mixture models for image segmentation," in Proc. British Machine Vision Conf., 2000, pp. 112-121.
17. Z. W. Tu, X. R. Chen, A. L. Yuille, and S. C. Zhu, "Image parsing: Unifying segmentation, detection and recognition," Int. J. Comput. Vis., vol. 63, pp. 113-140, 2005.