article

Hypotheses for Image Features, Icons and Textons

Authors:

Lewis D. Griffin,

Martin LillholmAuthors Info & Claims

International Journal of Computer Vision, Volume 70, Issue 3

Pages 213 - 230

https://doi.org/10.1007/s11263-006-6355-9

Published: 01 December 2006 Publication History

Abstract

We review ideas about the relationship between qualitative description of local image structure and quantitative description based on responses to a family of linear filters. We propose a sequence of three linking hypotheses. The first, the Feature Hypothesis, is that qualitative descriptions arise from a category system on filter-response space. The second, the Icon Hypothesis, is that the partitioning into categories of filter response space is determined by a system of iconic images, one associated with each point of the space. The third, the Texton Hypothesis, is that the correct images to play the role of icons are those that are the most likely explanations of a vector of filter responses. We present results in support of these three hypotheses, including new results on 2-D 1st order structure.

References

[1]

Barlow, H. B. 1953. Summation and inhibition in the frog's retina. Journal of Physiology (London) , 119:69-88.

[2]

Barlow, H. B. 1972. Single units and sensation: a neuron doctrine for perceptual psychology? Perception , 1:371-394.

[3]

Berlin, B. and Kay, P. 1969. Basic Color Terms: their Universality and Evolution , Berkeley: University of California Press.

[4]

Bimler, D. 2004. Personal Communication .

[5]

Buchsbaum, G. and Bloch, O. 2002. Color categories revealed by non-negative matrix factorization of Munsell color spectra. Vision Research , 42:559-563.

[6]

Cen, F., et al. 2004. Robust registration of 3-D ultrasound images based on gabor filter and mean-shift method. In Computer Vision and Mathematical Methods in Medical and Biomedical Image Analysis . p. 304-316.

[7]

Davidoff, J., Davies, I., and Roberson, D. 1999. Colour categories in a stone-age tribe. Nature , 398(6724):203-204.

[8]

Debnath, L. 1964. On Hermite Transforms. Mathematicki Vesnik , 1(16):285-292.

[9]

Debnath, L. 1995. Integral Transforms and their Applications , CRC Press.

[10]

DeValois, R. L., Abramov, I., and Jacobs, G. H. 1966. Analysis of response patterns of LGN cells. Journal of the Optical Society of America , 56:966-977.

[11]

Dowman, M. 2002. Modelling the acquisition of colour words. In Al 2002: Advances in Artificial Intelligence , p. 259-271.

[12]

Ellison, T. M. 2001. Induction and inherent similarity. In U. Hahn and M. Ramscar (Eds.) Similarity and Categorization , OUP: Oxford, p. 29-49.

[13]

Florack, L. M. J., et al. 1992. Families of Tuned Scale-Space Kernels. In Computer Vision - ECCV '92 , p. 19-23.

[14]

Gärdenfors, P. 2000. Conceptual Spaces: the geometry of thought , Cambridge MA: MIT Press.

[15]

Georgeson, M. A. and Freeman, T. C. A. 1997. Perceived location of bars and edges in one-dimensional images: Computational models and human vision. Vision Research , 37(1): 127-142.

[16]

Geusebroek, J. M., et al. 2003. Color constancy from physical principles. Pattern Recognition Letters , 24(11):1653-1662.

Digital Library

[17]

Gibson, J. J. 1979. The Ecological Approach to Visual Perception , Houghton Mifflin.

[18]

Griffin, L. D. 1995. Descriptions of Image Structure , London: PhD thesis, University of London.

[19]

Griffin, L. D. 1997. Critical Points in Affine Scale Space. In Gaussian Scale-Space Theory , S. Sporring, et al. (Ed.) p. 165-180.

[20]

Griffin, L. D. 2001. Similarity of Pyschological and Physical Colour Space shown by Symmetry Analysis. Color Research and Application , 26(2):151-157.

[21]

Griffin, L. D. 2002. Local image structure, metamerism, norms, and natural image statistics. Perception , 31(3):377-377.

[22]

Griffin, L. D. 2005. Feature classes for 1-D, 2nd order image structure arise from the maximum likelihood statistics of natural images. Network-Computation in Neural Systems , in press.

[23]

Griffin, L. D. and Colchester, A. C. F. 1995. Superficial and Deep-Structure in Linear Diffusion Scale-Space - Isophotes, Critical-Points and Separatrices. Image and Vision Computing , 13(7):543- 557.

[24]

Griffin, L. D. and Lillholm, M. 2003. Mode Estimation by Pessimistic Scale Space Tracking. In Scale Space '03 , Isle of Skye, UK: Springer.

[25]

Griffin, L. D. and Lillholm, M. 2005. Image features and the I-D, 2nd order gaussian derivative jet . In Proc. Scale Space 2005 . Springer. p. 26-37.

[26]

Griffin, L. D. and Lillholm, M. 2005. The multiscale mean shift algorithm for mode estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence , submitted.

[27]

Griffin, L. D., Lillholm, M. and Nielsen, M. 2004. Natural image profiles are most likely to be step edges. Vision Research , 44(4): 407-421.

[28]

Heiler, M. and Schnorr, C. 2005. Natural image statistics for natural image segmentation. International Journal of Computer Vision , 63(1):5-19.

Digital Library

[29]

Hering, E. 1920. Outlines of a theory of the light sense , Harvard: Harvard University Press.

[30]

Hubel, D. H. and Wiesel, T. N. 1968. Receptive fields and functional architecture of monkey striate cortex. Journal of Physiology , 195:215-243.

[31]

Hurvich, L. M. and Jameson, D. 1957. An opponent-process theory of color vision. Psychological Review , 64:384-404.

[32]

Jameson, K. A. 2005. Culture and Cognition: what is universal about color experience? Cognition and Culture , in press.

[33]

Kay, P. 2005. Color categories are not arbitrary. Cross-Cultural Research , 39(1):39-55.

[34]

Kay, P. and Maffi, L. 1999. Color appearance and the emergence and evolution of basic color lexicons. American Anthropologist , 101:743-760.

[35]

Kay, P. and McDaniel, C. K. 1978. The linguistic significance of the meanings of the basic color terms. Language , 54: 610-646.

[36]

Kay, P. and Regier, T. 2003. Resolving the question of color naming universals. Proceedings of the National Academy of Sciences of the United States of America

[37]

Kimmel, R. and Bruckstein, A. M. 2003. Regularized Laplacian Zero Crossings as Optimal Edge Integrators. International Journal of Computer Vision , 53(3):225-243.

Digital Library

[38]

Koenderink, J. J. 1984. The Structure of Images . Biological Cybernetics, 50(5):363-370.

[39]

Koenderink, J. J. 1988. Operational Significance of Receptive-Field Assemblies. Biological Cybernetics , 1 58(3):163-171.

[40]

Koenderink, J. J. 1993. What is a feature? Journal of Intelligent Systems , 3(1):49-82.

[41]

Koenderink, J. J. 2001. Multiple visual worlds (editorial). Perception , 30:1-7.

[42]

Koenderink, J. J. and van Doorn, A. J. 1987. Representation of Local Geometry in the Visual-System. Biological Cybernetics , 55(6): 367-375.

Digital Library

[43]

Koenderink, J. J. and van Doorn, A. J. 1990. Receptive-Field Families. Biological Cybernetics , 63(4):291-297.

Digital Library

[44]

Koenderink, J. J. and van Doorn, A. J. 1992. Generic Neighborhood Operators. Ieee Transactions on Pattern Analysis and Machine Intelligence , 14(6):597-605.

Digital Library

[45]

Koenderink, J. J. and van Doorn, A. J. 1992. Receptive Field Assembly Specificity. Journal of Visual Communication and Image Representation , 3(1):1-12.

Digital Library

[46]

Koenderink, J. J. and van Doorn, A. J. 1996. Metamerism in complete sets of image operators. In K. W. Bowyer and N. Ahuja (Eds.). Advances in Image Understanding: A Festschrift for Azriel Rosenfeld , Wiley-IEEE Computer Society Press, p. 113-129.

[47]

Koenderink, J. J. and van Doorn, A. J. 1997. Local Image Operators and Iconic Structure, In G. Sommer and J. J. Koenderink (Eds.). Algebraic Frames for the Perception-Action Cycle , Springer, p. 66-93.

[48]

Koenderink, J. J. and Van Doorn, A. J. 1998. The structure of relief, In Advances in Imaging and Electron Physics , 103:65-150.

[49]

Koenderink, J. J. and van Doorn, A. J. 2003. Perspectives on color space. In R. Mansfield and D. Heyer (Eds.). Colour Perception: Mind and the Physical World , OUP: Oxford, p. 1-56.

[50]

Lawson, S. and Zhu, J. 2000. Image compression using wavelets and JPEG2000: a tutorial. Electronics & Communication Engineering Journal , 14(3):112-121.

[51]

Lee, A. B., Pedersen, K. S., and Mumford, D. 2003. The nonlinear statistics of high-contrast patches in natural images. International Journal of Computer Vision , 54(1-2):83-103.

Digital Library

[52]

Leung, T. and Malik, J. 2001. Representing and recognizing the visual appearance of materials using three-dimensional textons. International Journal of Computer Vision , 43(1):29-44.

Digital Library

[53]

Lillholm, M., Nielsen, M., and Griffin, L. D. 2003. Feature-based image analysis. International Journal of Computer Vision , 52(2- 3):73-95.

[54]

Liu, X. W. and Wang, D. L. 2002. A spectral histogram model for texton modeling and texture discrimination. Vision Research , 42(23):2617-2634.

[55]

Logothetis, N. K., Pauls J., and Poggio, T. 1995. Shape Representation in the Inferior Temporal Cortex of Monkeys. Current Biology , 5(5):552-563.

[56]

Majthay, A. 1985. Foundations of Catastrophe Theory , London: Pitman Publishing Ltd.

[57]

Makram-Ebeid, S. and Mory, B. 2003. Scale-space image analysis based on hermite polynomials theory. In L. D. Griffin and M. Lillholm (Eds.). Proc. Conf. on Scale Space Methods in Computer Vision , Springer, p. 57-71.

[58]

Manmatha, R., Ravela, S., and Chitti, Y. 1998. On computing local and global similarity in images. In Human Vision and Electronic Imaging III , p. 540-551.

[59]

Marr, D. and Hildreth, E. 1980. Theory of edge detection. Proceedings of the Royal Society Series B , 20:187-217.

[60]

Marr, D., 1982, Vision . New York: W H Freeman & co.

[61]

Martens, J. B. 1997. Local orientation analysis in images by means of the Hermite transform. IEEE Transactions on Image Processing , 6(8): 1103-1116.

Digital Library

[62]

Martin, D. R., Fowlkes, C. C., and Malik, J. 2004. Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Transactions on Pattern Analysis and Machine Intelligence , 26(5):530-549.

Digital Library

[63]

Nakamura, K., et al. 1994. Visual Response Properties of Single Neurons in the Temporal Pole of Behaving Monkeys. Journal of Neurophysiology , 71(3):1206-1221.

[64]

Newton, I. 1706. Enumeratio linearum tertii ordinis .

[65]

Pedersen, K. S. 2003. Statistics of Natural Image Geometry. In Department of Computer Science , Copenhagen: University of Copenhagen.

[66]

Richards, W. 1979. Quantifying Sensory Channels - Generalizing Colorimetry to Orientation and Texture, Touch, and Tones. Sensory Processes , 3(3):207-229.

[67]

Rissanen, J. 1978. Modeling by shortest data description. Automatica , 14:465-471.

Digital Library

[68]

Rivero-Moreno, C. J. and Bres, S. 2003. Conditions of similarity between hermite and gabor filters as models of the human visual system. In N. Petkov and M. A. Westenberg (Eds.). Computer Analysis of Images and Patterns , Springer-Verlag, Berlin, p. 762-769.

[69]

Roberson, D. 2005. Color categories are culturally diverse in cognition as well as in language. Cross-Cultural Research , 39(1):56-71.

[70]

Scale Space '01. 2001. In Scale Space '01 . Vancouver, Canada: Springer.

[71]

Scale Space '03. 2003. In Scale Space '03 . Isle of Skye, UK: Springer.

[72]

Scale Space '05. 2005. In Scale Space '05 . Hofgeismar, Germany: Springer.

[73]

Scale Space '99. 1999. In Scale Space "99 , Corfu, Greece: Springer.

[74]

Sigala, N. and Logothetis, N. K. 2002. Visual categorization shapes feature selectivity in the primate temporal cortex. Nature , 415(6869):318-320.

[75]

Steels, L. and Belpaeme, T. 2005. Coordinating perceptually grounded categories through language. A case study for colour. Behavioral and Brain Sciences , In Press.

[76]

Tagliati, E. and Griffin, L. D. 2001. Features in Scale Space: Progress on the 2D 2nd Order Jet. In M. Kerckhove (Ed.). LNCS , Springer, p. 51-62.

[77]

ter Haar Romeny, B. M. 2003. Front-end Vision and Multi-Scale Image Analysis , Kluwer.

[78]

ter Haar Romeny, B. M. and Florack, L. M. J. 1994. Higher-order differential structure of images. Image and Vision Computing , 12(6): 317-325.

[79]

Thom, R. 1972. Structural stability and morphogenesis . Reading MA: W. A. Benjamin, Inc.

[80]

van den Boomgaard, R. 2003. Least squares and robust estimation of local image structure. In L. D. Griffin and M. Lillholm (Eds.). Proc. Scale Space Methods in Computer Vision , p. 237-254.

[81]

van Hateren, J. H. and van der Schaaf, A. 1998. Independent component filters of natural images compared with simple cells in primary visual cortex. Proceedings of the Royal Society of London Series B -Biological Sciences , 265(1394): 359-366.

[82]

van Trigt, C. 1990a. Smoothest Reflectance Functions. 1. Definition and Main Results. Journal of the Optical Society of America a-Optics Image Science and Vision , 7(10):1891-1904.

[83]

van Trigt, C. 1990b. Smoothest Reflectance Functions. 2. Complete Results. Journal of the Optical Society of America a-Optics Image Science and Vision , 7(12):2208-2222.

[84]

Varma, M. and Zisserman, A. 2002. Classifying images of materials: achieving viewpoint and illumination independence. In ECCV '02 , Copenhagen, Springer.

[85]

Varma, M. and Zisserman, A. 2005. A statistical approach to texture classification from single images. International Journal of Computer Vision , 62(1-2):61-81.

Digital Library

[86]

Vogels, R., et al., 2001. Inferior temporal neurons show greater sensitivity to nonaccidental than to metric shape differences. Journal of Cognitive Neuroscience , 13(4):444-453.

Digital Library

[87]

Wilson, M. and Debauche, B. A. 1981. Inferotemporal Cortex and Categorical Perception of Visual-Stimuli by Monkeys. Neuropsychologia , 19(1):29-41.

[88]

Wu, S. W. and Gersho, A. 1993. Lapped Vector Quantization of Images. Optical Engineering , 32(7):1489-1495.

[89]

Yendrikhovskij, S. N. 2001. Computing color categories from statistics of natural images. Journal of Imaging Science and Technology , 45(5):409-417.

[90]

Young, R. A. 1987. The Gaussian derivative model for spatial vision: I. Retinal mechanisms. Spatial Vision , 2:273-293.

[91]

Young, R. A. and Lesperance, R. M. 2001. The Gaussian Derivative model for spatial-temporal vision: II. Cortical data. Spatial Vision , 14(3-4):321-389.

[92]

Young, R. A., Lesperance, R.M., and Meyer, W. W. 2001. The Gaussian Derivative model for spatial-temporal vision: I. Cortical model. Spatial Vision , 14(3-4):261-319.

[93]

Zhilkin, P. and Alexander, M. E. 2000. 3D image registration using a fast noniterative algorithm. Magnetic Resonance Imaging , 18(9):1143-1150.

[94]

Zhu, S.-C., et al. 2005. What are textons? International Journal of Computer Vision , 62(1):121-143.

Digital Library

Cited By

Fernández AÁlvarez MBianconi F(2013)Texture Description Through Histograms of Equivalent PatternsJournal of Mathematical Imaging and Vision10.1007/s10851-012-0349-845:1(76-102)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1007/s10851-012-0349-8
Griffin L(2008)Symmetries of 1-D ImagesJournal of Mathematical Imaging and Vision10.1007/s10851-008-0078-131:2-3(157-164)Online publication date: 1-Jul-2008
https://dl.acm.org/doi/10.1007/s10851-008-0078-1
Lillholm MGriffin L(2007)Maximum likelihood metameres for local 2nd order image structure of natural imagesProceedings of the 1st international conference on Scale space and variational methods in computer vision10.5555/1767926.1767966(394-405)Online publication date: 30-May-2007
https://dl.acm.org/doi/10.5555/1767926.1767966
Show More Cited By

Index Terms

Hypotheses for Image Features, Icons and Textons
1. Computing methodologies
2. Theory of computation
  1. Design and analysis of algorithms

Recommendations

Face recognition with local gabor textons
ICB'07: Proceedings of the 2007 international conference on Advances in Biometrics

This paper proposes a novel face representation and recognition method based on local Gabor textons. Textons, defined as a vocabulary of local characteristic features, are a good description of the perceptually distinguishable micro-structures on ...
What Are Textons?
ECCV '02: Proceedings of the 7th European Conference on Computer Vision-Part IV

Textons refer to fundamental micro-structures in generic natural images and thus constitute the basic elements in early (preattentive) visual perception. However, the word "texton" remains a vague concept in the literature of computer vision and visual ...
What are Textons?
Special Issue on Texture Analysis and Synthesis

Textons refer to fundamental micro-structures in natural images (and videos) and are considered as the atoms of pre-attentive human visual perception (Julesz, 1981). Unfortunately, the word "texton" remains a vague concept in the literature for lack of a ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image International Journal of Computer Vision

International Journal of Computer Vision Volume 70, Issue 3

December 2006

100 pages

ISSN:0920-5691

Issue’s Table of Contents

Copyright © Copyright © 2006 Springer Science + Business Media, LLC.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 December 2006

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 03 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Fernández AÁlvarez MBianconi F(2013)Texture Description Through Histograms of Equivalent PatternsJournal of Mathematical Imaging and Vision10.1007/s10851-012-0349-845:1(76-102)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.1007/s10851-012-0349-8
Griffin L(2008)Symmetries of 1-D ImagesJournal of Mathematical Imaging and Vision10.1007/s10851-008-0078-131:2-3(157-164)Online publication date: 1-Jul-2008
https://dl.acm.org/doi/10.1007/s10851-008-0078-1
Lillholm MGriffin L(2007)Maximum likelihood metameres for local 2nd order image structure of natural imagesProceedings of the 1st international conference on Scale space and variational methods in computer vision10.5555/1767926.1767966(394-405)Online publication date: 30-May-2007
https://dl.acm.org/doi/10.5555/1767926.1767966
Loog M(2007)The jet metricProceedings of the 1st international conference on Scale space and variational methods in computer vision10.5555/1767926.1767930(25-31)Online publication date: 30-May-2007
https://dl.acm.org/doi/10.5555/1767926.1767930
Griffin L(2007)The Second Order Local-Image-Structure SolidIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2007.106629:8(1355-1366)Online publication date: 1-Aug-2007
https://dl.acm.org/doi/10.1109/TPAMI.2007.1066

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents