Learning higher-order structures in natural images

Network: Computation in Neural Systems

Volumn 14, Issue 3, 2003, Pages 483-499

  Karklin, Yan ^a   Lewicki, Michael S ^a  

^a Carnegie Mellon University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHM; ARTIFICIAL NEURAL NETWORK; CALCULATION; CONFERENCE PAPER; IMAGE ANALYSIS; LEARNING; RETINA IMAGE; STATISTICAL ANALYSIS; STATISTICAL MODEL; VISION;

EID: 0347517574     PISSN: 0954898X     EISSN: None     Source Type: Journal    
DOI: 10.1088/0954-898X_14_3_306     Document Type: Conference Paper

References (27)

1. Barlow HB 1961 Possible principles underlying the transformation of sensory messages Sensory Communication ed W A Rosenbluth (Cambridge, MA: MIT Press) pp 217-34
2. Bell A J and Sejnowski T J 1995 An information maximization approach to blind separation and blind deconvolution Neural Comput. 7 1129-59
3. Bell A J and Sejnowski T J 1997 The 'independent components' of natural scenes are edge filters Vis. Res. 37 3327-38
4. Cardoso J-F 1997 Infomax and maximum likelihood for blind source separation IEEE Signal Process. Lett. 4 109-11
5. Chen S, Donoho D L and Saunders M A 1996 Atomic decomposition by basis pursuit Technical Report Dept. Stat. Stanford University, Stanford, CA
6. Coifman R R and Wickerhauser M V 1992 Entropy-based algorithms for best basis selection IEEE Trans. Inf. Theory 38 713-18
7. Comon P 1994 Independent component analysis, a new concept? Signal Process. 36 287-314
8. Field D J 1994 What is the goal of sensory coding? Neural Comput. 6 559-601
9. Geisler W S, Perry J S, Super B J and Gallogly D P 2001 Edge co-occurence in natural images predicts contour grouping performance Vis. Res. 41 711-24
10. Hoyer P O and Hyvarinen A 2002 A multi-layer sparse coding network learns contour coding from natural images Vis. Res. 42 1593-605
11. Hyvarinen A and Hoyer P 2000 Emergence of phase-and shift-invariant features by decomposition of natural images into independent feature subspaces Neural Comput. 12 1705-20
12. Hyvarinen A and Hoyer P O 2001 A two-layer sparse coding model learns simple and complex cell receptive fields and topography from natural images Vis. Res. 41 2413-23
13. Hyvarinen A, Hoyer P O and Inki M 2001 Topographic independent component analysis Neural Comput. 13 1527-58
14. Krüger N 1998 Colinearity and parallelism are statistically significant second order relations of complex cell responses Neural Process. Lett. 8 117-29
15. Lee T-W and Lewicki M S 2002 Unsupervised classification, segmentation and de-noising of images using ICA mixture models IEEE Trans. Image Process. 11 270-9
16. Lee T-W, Lewicki M S and Sejnowski T J 2000 ICA mixture models for unsupervised classification of non-Gaussian sources and automatic context switching in blind signal separation IEEE Trans. Pattern Anal. Mach. Intell. 22 1078-89
17. Lee T-W, Wachtler T and Sejnowski T 2002 Color opponency is an efficient representation of spectral properties in natural scenes Vis. Res. 42 2095-103
18. Lewicki M S and Olshausen B A 1999 A probabilistic framework for the adaptation and comparison of image codes J. Opt. Soc. Am. A 16 1587-601
19. Lewicki M S and Sejnowski T J 2000 Learning overcomplete representations Neural Comput. 12 337-65
20. Olshausen B A and Field D J 1996 Emergence of simple-cell receptive-field properties by learning a sparse code for natural images Nature 381 607-9
21. Olshausen B A and Field D J 1997 Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis. Res. 37 3311-25
22. Pearlmutter B A and Parra L C 1996 A context-sensitive generalization of ICA Int. Conf. on Neural Information Processing (NIPS 1996, Hong Kong, September 1996) vol 9, ed M Mozer, M Jordan and T Petsche (Cambridge, MA: MIT Press) pp 151-7, http://nips.djvuzone.org
23. Schwartz O and Simoncelli E P 2001 Natural signal statistics and sensory gain control Nat. Neurosci. 4 819-25
24. Sigman M, Cecchi G A, Gilbert C D and Magnasco M O 2001 On a common circle: natural scenes and gestalt rules Proc. Natl Acad. Sci. USA 98 1935-40
25. Simoncelli E and Olshausen B 2001 Natural image statistics and neural representation Annu. Rev. Neurosci. 24 1193-216
26. van Hateren J H and Ruderman D L 1998 Independent component analysis of natural image sequences yield spatiotemporal filters similar to simple cells in primary visual cortex Proc. R. Soc. B 265 2315-20
27. van Hateren J H and van der Schaaf A 1998 Independent component filters of natural images compared with simple cells in primary visual cortex Proc. R. Soc. B 265 359-66