Modeling visual patterns by integrating descriptive and generative methods

International Journal of Computer Vision

Volumn 53, Issue 1, 2003, Pages 5-29

  Guo, Cheng En ^a   Zhu, Song Chun ^a   Wu, Ying Nian ^b  

^a Engineering IV   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Descriptive models; Generative models; Gibbs point processes; Markov chain Monte Carlo; Markov random fields; Minimax entropy learning; Perceptual organization; Texton models; Visual learning

Indexed keywords

COMPUTER SIMULATION; COMPUTER VISION; IMAGE ANALYSIS; LEARNING SYSTEMS; MARKOV PROCESSES; MATHEMATICAL MODELS; MONTE CARLO METHODS; PRINCIPAL COMPONENT ANALYSIS; WAVELET TRANSFORMS;

DESCRIPTIVE MODELS; GENERATIVE MODELS; GIBBS POINT PROCESSES; MINIMAX ENTROPY LEARNING; TEXTON MAPS;

PATTERN RECOGNITION;

EID: 0038243248     PISSN: 09205691     EISSN: None     Source Type: Journal    
DOI: 10.1023/A:1023023207396     Document Type: Article

References (44)

1. Ahuja, N. and Tuceryan, M. 1989. Extraction of early perceptual struct. in dot patterns. CVGIP, 48.
2. Bell, A.J. and Sejnowski, T.J. 1997. The independent components of natural images are edge filters. Vision Research, 37:3327-3338.
3. Bergen, J. and Adelson, E. 1988. Early vision and texture perception. Nature, 333:363-364.
4. Chandler, D. 1987. Introduction to Modern Statistical Mechanics, Oxford University Press.
5. Chen, S., Donoho, D., and Saunders, M.A. 1999. Atomic decomposition by basis pursuit. SIAM Journal on Scientific Computing, 20(1):33-61.
6. Cover, T. and Thomas, J.A. 1994. Elements of Information Theory.
7. De Bonet, J. S. and Viola, P. 1997. A non-parametric multi-scale statistical model for natural images. Advances in Neural Information Processing, 10.
8. Dempster, A.P., Laird, N.M., and Rubin, D.B. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B, 39:1-38.
9. Duda, R., Hart P. and Stork D. 2000. Pattern Classification and Scene Analysis, 2nd edn., John Wiley & Sons.
10. Efros, A.A. and Freeman, W.T. 2001. Image quilting for texture synthesis and transfer. SIGGRAPH.
11. Frey, B. and Jojic, N. 1999. Transformed component analysis: Joint estimation of spatial transforms and image components. ICCV.
12. Fridman, A. 2000. Mixed Markov Models, Doctoral dissertation. Division of Applied Math, Brown University.
13. Geman, S. and Geman, D. 1984. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. PAMI 6:721-741.
14. Gilks, W.R. and Roberts, R.O. 1997. Strategies for improving MCMC, chapter 6 in W.R. Gilks et al. (Eds) Markov Chain Monte Carlo in Practice, Chapman & Hall.
15. Green, P.J. 1995. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika, 82:711-732.
16. Gu, M.G. 1998. A stochastic approximation algorithm with MCMC method for incomplete data estimation problems. Preprint, Dept. of Math. and Stat., McGill Univ.
17. Heeger, D.J. and Bergen, J.R. 1995. Pyramid-based texture analysis/synthesis. SIGGRAPHS.
18. Jaynes, E.T. 1957. Information theory and statistical mechanics. Physical Review 106:620-630.
19. Julesz, B. 1981. Textons, the elements of texture perception and their interactions. Nature, 290:91-97.
20. Koffka, K. 1935. Principles of Gestalt Psychology.
21. Lee, A.B., Mumford, D.B., and Huang, J.G. 2001. Occlusion models for natural images: A statistical study of a scale-invariant dead leaves model. Int'l J. of Computer Vision, 41(1/2):35-59.
22. Leung, T. and Malik, J. 1996. Detecting, localizing and grouping repeated scene elements from an image. In Proc. 4th ECCV, Cambridge, UK.
23. Leung, T. and Malik, J. 1999. Recognizing surface using three-dimensional textons. In Proc. of 7th ICCV, Corfu, Greece.
24. Lewicki, M.S. and Olshausen, B.A. 1999. A probabilistic framework for the adaptation and comparison of image codes. JOSA, A, 16(7):1587-1601.
25. Malik, J. and Perona, P. 1990. Preattentive texture discrimination with early vision mechanisms. J. of Optical Society of America A, 7(5).
26. Malik, J., Belongie, S., Shi, J., and Leung, T. 1999. Textons, contours and regions: Cue integration in image segmentation. ICCV.
27. Mallat, S. and Zhang, Z. 1993. Matching pursuit in a time-frequency dictionary. IEEE Trans. on Signal Processing, 41:3397-3415.
28. Marr, D. Vision. W.H. Freeman and Company.
29. Miller, E.G. 2002. Learning from one example in machine vision by sharing probability densities. Ph.D. Thesis, Massachusetts Institute of Technology.
30. Olshausen, B.A. and Field, D.J. 1996. Emergence of simple-cell receptive field properties by learning a sparse code for natural images" Nature, 381:607-609.
31. Roweis, S. and Ghahramani, Z. 1999. A unifying review of linear Gaussian models. Neural Computation, 11(2).
32. Portilla, J. and Simoncelli, E.P. 2000. A parametric texture model based on joint statistics of complex wavelet coefficients. IJCV, 40(1):47-70.
33. Steven, K.A. 1978. Computation of locally parallel structure. Biol. Cybernetics, 29:19-28.
34. Stoyan, D., Kendall, W.S., and Mecke, K. 1985. Stochastic Geometry and its Applications.
35. Tanner, M. 1996. Tools for Statistical Inference. Springer.
36. Tu, Z. and Zhu, S.C. 2002. Image Segmentation by Data-Driven Markov Chain Monte Carlo. IEEE Trans. PAMI, 24(5):657-673.
37. Wu, Y.N., Zhu, S.C., and Liu, X.W. 1999. Equivalence of Julesz and Gibbs Ensembles. ICCV.
38. Wu, Y.N., Zhu, S.C., and Guo, C. 2002. Statistical modeling of texture sketch. ECCV.
39. Xu, Y.Q., Guo, B.N., and Shum, H.Y. 2000. Chaos mosaic: Fast and memory efficient texture synthesis. MSR TR-2000-32.
40. Zhu, S.C. 1999. Embedding Gestalt Laws in Markov Random Fields. IEEE Trans. PAMI. 21(11):1170-1187.
41. Zhu, S.C. and Guo, Cheng-en. 2001. Conceptualization and modeling of visual patterns. In Proc. of 3rd Int'l Workshop on Perceptual Organization in Computer Vision, Vancouver, Canada.
42. Zhu, S.C., Guo, C., Wu, Y.N., and Wang, Y. 2002. What are textons? ECCV.
43. Zhu, S.C., Liu, X.W., and Wu, Y.N. 2000. Exploring Julesz texture ensemble by effective Markov Chain Monte Carlo. IEEE Trans. PAMI, 22(6):554-569.
44. Zhu, S.C., Wu, Y.N., and Mumford, D. 1997. Minimax entropy principle and its application to texture modeling. Neural Computation, 9(8):1627-1660.