Form of representation:
- B. A. Olshausen and D. J. Field, Emergence of simple-cell receptive field properties
by learning a sparse code for natural images, Nature, 381, 607-609, 1996.
- S. C. Zhu, C. E. Guo, Y. Z. Wang, and Z. J. Xu, What are
textons, International Journal of Computer Vision, 62,
121-143, 2005.
- S. C. Zhu and D. B. Mumford, A stochastic grammar of images, Foundations and
Trends in Computer Graphics and Vision, 2, 259-362, 2006.
Scheme of learning:
- S. Mallat and Z. Zhang, Matching pursuit in a time-frequency dictionary, IEEE
Transactions on Signal Processing, 41, 3397-415, 1993.
- J. H. Friedman, Exploratory projection pursuit, Journal of the American Statistical
Association, 82, 249-266, 1987.
- Y. Freund and R. E. Schapire, A decision-theoretic generalization of on-line
learning and an application to boosting, Journal of Computer and System Sciences,
55, 119-139.
- P. A. Viola and M. J. Jones, Robust real-time face detection, International
Journal of Computer Vision, 57, 137-154, 2004.
- S. C. Zhu, Y. N. Wu, and D. B. Mumford, Minimax entropy principle and its applications
in texture modeling, Neural Computation, 9, 1627-1660, 1997.
- S. Della Pietra, V. Della Pietra, and J. Lafferty, Inducing features of random fields,
IEEE Transactions on Pattern Analysis and Machine Intelligence, 19, 380-393, 1997.
Architecture of inference:
- M. Riesenhuber and T. Poggio, Hierarchical models of object recognition in cortex,
Nature Neuroscience, 2, 1019-1025,
1999.
- Sparsity of Olshausen-Field (sparsity seems to subsum compositionality
and invariance).
- Compositionality of S. Geman; Zhu-Mumford (the concept of parts cannot be
defined without sparsity).
- Invariance of Riesenhuber-Poggio (accomplished by maxing out the OR-nodes in
Zhu-Mumford's and-or graph for vision).
Our work is a continuation of our long term search for generative models and
model-based algorithms, as well as our attempt to understand these models within
a common information-theoretical framework. The active basis model can be considered
a revision of our previous model on textons. It can also be viewed as an inhomogeneous
version of the Markov random field model that we previously developed for textures.
More important, the active basis model is a simplest instance of the and-or graph
in the compositional framework that we have been studying. The and-or grammar naturally
suggests to further compose multiple active bases to represent more articulate shapes.
The architecture of the sum-max maps is a natural computational tool for parsing the
observed image according to the and-or grammar.
- Wu, Y. N., Guo, C., and Zhu, S. C. (2008) From information scaling to regimes of
statistical models. Quarterly of Applied Mathematics, 66, 81-122.
pdf
latex
ppt
- Wu, Y. N., Si, Z., Fleming, C., and Zhu, S. C. (2007) Deformable template as
active basis. Proceedings of International Conference of Computer Vision.
pdf
latex
ppt
- Wu, Y. N., Li, J., Liu, Z., and Zhu, S. C. (2007) Statistical principles in image
modeling. Technometrics, 49, 249-261.
pdf
latex
- Guo, C., Zhu, S. C. and Wu, Y. N. (2007) Primal sketch: integrating structure
and texture. Computer Vision and Image Understanding, 106, 5-19.
pdf
latex
ppt
- Zhu, S. C. and Mumford, D. (2006) A stochastic grammar of images. Foundations
and Trends in Computer Graphics and Vision, 2, 259-362.
pdf
- Zhu S. C., Guo C., Wang Y, and Xu Z. (2005) What are textons? International
Journal of Computer Vision, 62, 21-143.
pdf
- Zhu S. C. (2003) Statistical modeling and conceptualization of visual patterns.
IEEE Pattern Analysis and Machine Intelligence, 25. 691-712.
pdf
- Guo, C., Zhu, S. C., and Wu, Y. N. (2003) Towards a mathematical theory of primal
sketch and sketchability. Proceedings of International Conference of Computer Vision.
1228-1235.
pdf
latex
ppt
- Guo, C., Zhu, S. C., and Wu, Y. N. (2003) Visual learning by integrating descriptive
and generative models. International Journal of Computer Vision. 53, 5-29.
pdf
- Zhu, S. C., Guo, C., Wu, Y. N. and Wang, Y. (2002) What are textons? Proceedings of
European Conference of Computer Vision, 793-807.
pdf
- Wu, Y. N., Zhu, S. C., and Guo, C. (2002) Statistical modeling of texture sketch.
Proceedings of European Conference of Computer Vision, 240-254.
pdf
latex
ppt
- Wu, Y. N. and Zhu, S. C. (2001) Vision and the art of data augmentation. Discussion of a paper
by Meng and van Dyk. Journal of Computational and Graphical Statistics, 10, 90-93.
pdf
- Wu, Y. N., Zhu, S. C., and Liu, X. (2000) Equivalence of Julesz ensembles and
FRAME models. International Journal of Computer Vision, 38, 245-261.
pdf
- Zhu, S. C., Liu, X., and Wu, Y. N. (2000) Exploring texture ensembles by efficient
Markov chain Monte Carlo - towards a `trichromacy' theory of texture. IEEE Pattern
Analysis and Machine Intelligence, 22, 554-569.
pdf
- Zhu, S. C., Wu, Y. N., and Mumford, D. B. (1998) Minimax entropy principle and its
application to texture modeling. Neural Computation, 9, 1627-1660.
pdf
- Zhu, S. C. and Mumford, D. (1997) Prior learning and Gibbs reaction-diffusion.
IEEE Pattern Analysis and Machine Intelligence, 19, 1236-1250.
pdf
- Zhu, S. C., Wu, Y. N., and Mumford, D. B. (1997) Filter, Random field, And Maximum
Entropy (FRAME): towards a unified theory for texture modeling. International Journal of
Computer Vision, 27, 107-126.
pdf
- Zhu, S. C. and Yuille, A. L. (1996) A flexible object recognition and modeling system.
International Journal of Computer Vision, 20, 187-212.
pdf
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