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Journal of the Optical Society of America A

Journal of the Optical Society of America A

| OPTICS, IMAGE SCIENCE, AND VISION

  • Editor: Franco Gori
  • Vol. 29, Iss. 7 — Jul. 1, 2012
  • pp: 1313–1345

Local image statistics: maximum-entropy constructions and perceptual salience

Jonathan D. Victor and Mary M. Conte  »View Author Affiliations


JOSA A, Vol. 29, Issue 7, pp. 1313-1345 (2012)
http://dx.doi.org/10.1364/JOSAA.29.001313


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Abstract

The space of visual signals is high-dimensional and natural visual images have a highly complex statistical structure. While many studies suggest that only a limited number of image statistics are used for perceptual judgments, a full understanding of visual function requires analysis not only of the impact of individual image statistics, but also, how they interact. In natural images, these statistical elements (luminance distributions, correlations of low and high order, edges, occlusions, etc.) are intermixed, and their effects are difficult to disentangle. Thus, there is a need for construction of stimuli in which one or more statistical elements are introduced in a controlled fashion, so that their individual and joint contributions can be analyzed. With this as motivation, we present algorithms to construct synthetic images in which local image statistics—including luminance distributions, pair-wise correlations, and higher-order correlations—are explicitly specified and all other statistics are determined implicitly by maximum-entropy. We then apply this approach to measure the sensitivity of the human visual system to local image statistics and to sample their interactions.

© 2012 Optical Society of America

OCIS Codes
(330.5000) Vision, color, and visual optics : Vision - patterns and recognition
(330.5510) Vision, color, and visual optics : Psychophysics

ToC Category:
Vision, Color, and Visual Optics

History
Original Manuscript: January 5, 2011
Revised Manuscript: December 8, 2011
Manuscript Accepted: December 16, 2011
Published: June 14, 2012

Virtual Issues
Vol. 7, Iss. 9 Virtual Journal for Biomedical Optics

Citation
Jonathan D. Victor and Mary M. Conte, "Local image statistics: maximum-entropy constructions and perceptual salience," J. Opt. Soc. Am. A 29, 1313-1345 (2012)
http://www.opticsinfobase.org/josaa/abstract.cfm?URI=josaa-29-7-1313


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References

  1. C. Chubb, M. S. Landy, and J. Econopouly,“A visual mechanism tuned to black,” Vis. Res. 44, 3223–3232.(2004).
  2. B. Julesz,“Textons, the elements of texture perception, and their interactions,” Nature 290, 91–97.(1981). [CrossRef]
  3. B. Julesz, E. N. Gilbert, and J. D. Victor,“Visual discrimination of textures with identical third-order statistics,” Biol. Cybern. 31, 137–140.(1978).
  4. J. D. Victor and M. M. Conte,“Spatial organization of nonlinear interactions in form perception,” Vis. Res. 31, 1457–1488 (1991). [CrossRef]
  5. S. Lyu and E. P. Simoncelli,“Nonlinear extraction of independent components of natural images using radial gaussianization,” Neural Comput. 21, 1485–1519 (2009). [CrossRef]
  6. F. Sinz, E. P. Simoncelli, and M. Bethge, “Hierarchical modeling of local image features through Lp-nested symmetric distributions,” in Advances in Neural Information Processing Systems, Vol. 22, Y. Bengio, D. Schuurmans, J. Lafferty, C. K. I. Williams, and A. Culotta, eds. (NIPS, 2009), pp. 1696–1704.
  7. D. J. Field, “Relations between the statistics of natural images and the response properties of cortical cells,” J. Opt. Soc. Am. A 4, 2379–2394 (1987).
  8. W. A. Richards, “Lightness scale from image intensity distributions,” Appl. Opt. 21, 2569–2582 (1982).
  9. C. Zetzsche and G. Krieger, “Nonlinear mechanism and higher-order statistics in biological vision and electronic image processing: review and perspectives,” J. Electron. Imaging 10, 56–99 (2001).
  10. G. Tkacik, J. S. Prentice, J. D. Victor, and V. Balasubramanian, “Local statistics in natural scenes predict the saliency of synthetic textures,” Proc. Natl. Acad. Sci. U.S.A. 107, 18149–18154 (2010). [CrossRef]
  11. A. Torralba and A. Oliva, “Statistics of natural image categories,” Network 14, 391–412 (2003). [CrossRef]
  12. W. S. Geisler, “Visual perception and the statistical properties of natural scenes,” Annu. Rev. Psychol. 59, 167–192 (2008).
  13. S. V. David and J. L. Gallant, “Predicting neuronal responses during natural vision,” Network 16, 239–260 (2005). [CrossRef]
  14. H. B. Barlow, “Possible principles underlying the transformation of sensory messages,” in Sensory Communication, W. A. Rosenblith, ed. (MIT, 1961), pp. 217–234.
  15. R. Levine and M. Tribus, The Maximum Entropy Formalism (MIT, 1979).
  16. T. M. Cover and J. A. Thomas, Elements of Information Theory (Wiley, 1991).
  17. E. Schneidman, M. J. Berry, R. Segev, and W. Bialek, “Weak pair-wise correlations imply strongly correlated network states in a neural population,” Nature 440, 1007–1012(2006). [CrossRef]
  18. S. H. Nirenberg and J. D. Victor, “Analyzing the activity of large populations of neurons: how tractable is the problem?” Curr. Opin. Neurobiol. 17, 397–400 (2007).
  19. J. Shlens, G. D. Field, J. L. Gauthier, M. I. Grivich, D. Petrusca, A. Sher, A. M. Litke, and E. J. Chichilnisky, “The structure of multi-neuron firing patterns in primate retina,” J. Neurosci. 26, 8254–8266 (2006).
  20. S. C. Zhu, Y. Wu, and D. Mumford, “Filters, random fields and maximum entropy (FRAME): towards a unified theory for texture modeling,” Int. J. Comput. Vis. 27, 107–126(1998). [CrossRef]
  21. D. K. Pickard, “Unilateral Markov fields,” Adv. Appl. Probab. 12, 655–671 (1980). [CrossRef]
  22. J. D. Victor, C. Chubb, and M. M. Conte, “Interaction of luminance and higher-order statistics in texture discrimination,” Vis. Res. 45, 311–328 (2005). [CrossRef]
  23. J. D. Victor, A. Ashurova, C. Chubb, and M. M. Conte, “Isodiscrimination contours in a three-parameter texture space,” J. Vis. 6(6):205, 205a, http://journalofvision.org/6/6/205/ , (2005). [CrossRef]
  24. S. I. Amari, “Information geometry on hierarchy of probability distributions,” IEEE Trans. Inf. Theory 47, 1701–1711(2001). [CrossRef]
  25. F. Champagnat, J. Idier, and Y. Goussard, “Stationary Markov Random Fields on a finite rectangular lattice,” IEEE Trans. Inf. Theory 44, 2901–2916 (1998). [CrossRef]
  26. N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, and E. Teller, “Equations of state calculations by fast computing machines,” J. Chem. Phys. 21, 1087–1091 (1953). [CrossRef]
  27. P. Brodatz, Textures: a Photographic Album for Artists and Designers (Dover, 1965).
  28. A. Poirson, B. Wandell, D. Varner, and D. Brainard, “Surface characterizations of color thresholds,” J. Opt. Soc. Am. A 7, 783–789 (1990).
  29. J. Portilla and E. Simoncelli, “A parametric texture model based on joint statistics of complex wavelet coefficients,”. Int. J. Comput. Vis. 40, 49–71 (2000).
  30. J. Shlens, G. D. Field, J. L. Gauthier, M. I. Grivich, D. Petrusca, A. Sher, A. M. Litke, and E. J. Chichilnisky, “The structure of multi-neuron firing patterns in primate retina,” J. Neurosci. 26, 8254–8266 (2006). [CrossRef]

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