Skip to main content
SpringerLink
Log in

The Visual Hierarchy Mirage: Seeing Trees in a Graph

  • Chapter
Shape Perception in Human and Computer Vision

Part of the book series: Advances in Computer Vision and Pattern Recognition ((ACVPR))

Abstract

The lure of hierarchies is almost irresistable. They provide elegantly simple representations that organize data for efficient search, or processors for communication. Since many current computer vision systems are focused on recognition, it is perhaps not surprising that they are also organized hierarchically. Although some success in object recognition has been achieved, it is notably within restricted problem domains. Fragility near the domain boundaries remains a problem. An analogy with biological vision systems suggests the limitation lies in the area of perceptual organization, or those processes that abstract image constructs into task-relevant terms. For machine vision applications this organization is subsumed by domain (image) preselection. Using border ownership as an example, we show that in biological vision this task is (necessarily) solved by non-hierarchical processing organizations. If the whole computer vision system is truly to be more than a sum of parts, we need to move from trees to the graph that they are approximating.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Simoncelli E, Olshausen BA (2001) Annu Rev Neurosci 24:1193

    Article  Google Scholar 

  2. Schwartz O, Pillow JW, Rust NC, Simoncelli EP (2006) J Vis 6:484

    Article  Google Scholar 

  3. Lamme VA (2003) Trends Cogn Sci 7(1):12

    Article  Google Scholar 

  4. Itti L, Rees G, Tsotsos J (eds) (2005) Elsevier/Academic Press

    Google Scholar 

  5. Serre T, Wolf L, Bileschi S, Riesenhuber M, Poggio T (2007) IEEE Trans Pattern Anal Mach Intell 29(3):411

    Article  Google Scholar 

  6. Fidler S, Boben M, Leonardis A (2009) In: Object categorization: computer and human vision perspectives. Cambridge University Press, Cambridge. http://vicos.fri.uni-lj.si/data/alesl/chapterLeonardis.pdf

    Google Scholar 

  7. Lecun Y, Bengio Y (1995) In: Arbib MA (ed) The handbook of brain theory and neural networks. MIT Press, Cambridge

    Google Scholar 

  8. Hinton GE, Osindero S, Teh YW (2006) Neural Comput 18:1527

    Article  MathSciNet  MATH  Google Scholar 

  9. Mori G, Ren X, Efros A, Malik J (2004) In: Proceedings of the 2004 IEEE computer society conference on computer vision and pattern recognition, 2004, CVPR 2004, vol 2, pp II-326–II-333. doi:10.1109/CVPR.2004.1315182

    Chapter  Google Scholar 

  10. Yao BZ, Yang X, Lin L, Lee MW, Zhu SC (2010) Proc IEEE 98(8):1485

    Article  Google Scholar 

  11. Olshausen BA, Field D (2006) In: van Hemmen J, Sejnowski T (eds) 23 problems in systems neuroscience. Oxford University Press, Oxford. doi:10.1093/acprof:oso/9780195148220.001.0001

    Google Scholar 

  12. Pinto N, Cox DD, DiCarlo JJ (2008) PLoS Comput Biol 4(1):e27. doi:10.1371/journal.pcbi.0040027

    Article  MathSciNet  Google Scholar 

  13. Ungerleider LG, Mishkin M (1982) In: Ingle D, Goodale MA, Mansfield R (eds) Analysis of visual behavior. MIT Press, Cambridge

    Google Scholar 

  14. Goodale M, Milner AD (1992) Trends Neurosci 15(1):20

    Article  Google Scholar 

  15. Desimone R, Schein S (1987) J Neurophysiol 57(3):835

    Google Scholar 

  16. Wertheimer M (1923) Psychol Forsch 4:301

    Article  Google Scholar 

  17. Koffka K (1935) Principles of Gestalt psychology. Harcourt, Brace & World, New York

    Google Scholar 

  18. Nakayama K, Shimojo S (1992) Science 257(5075):1357

    Article  Google Scholar 

  19. Felleman D, Essen DV (1991) Cereb Cortex 1:1

    Article  Google Scholar 

  20. Lindberg DC (1996) Theories of vision from Al-kindi to Kepler. University of Chicago Press, Chicago

    Google Scholar 

  21. Selfridge O (1959) In: Laboratory NP (ed) Symposium on the mechanization of thought processes. H. M. Stationary Office, London

    Google Scholar 

  22. Hubel DH, Wiesel TN (1977) Proc R Soc Lond B 198:1

    Article  Google Scholar 

  23. Gross CG (2002) Neuroscientist 8(5):512. doi:10.1177/107385802237175

    Article  Google Scholar 

  24. Fukushima K (1980) Biol Cybern 36:193

    Article  MathSciNet  MATH  Google Scholar 

  25. Rosenfeld A, Thurston M (1971) IEEE Trans Comput C-20:562

    Article  Google Scholar 

  26. Marr D, Hildreth E (1979) Theory of edge detection. Tech. Rep. MIT AI Memo 518, MIT AI Lab

    Google Scholar 

  27. Koenderink JJ (1984) Biol Cybern 50:363

    Article  MathSciNet  MATH  Google Scholar 

  28. Witkin AP (1983) In: Proceedings of the 8th international joint conference on artificial intelligence, Karlsruhe, West Germany, pp 1019–1022

    Google Scholar 

  29. Lindeberg T (1994) Scale-space theory in computer vision. Kluwer Academic, Norwell

    Book  Google Scholar 

  30. Caselles V, Morel JM, Sbert C (1998) IEEE Trans Image Process 7(3):376

    Article  MathSciNet  MATH  Google Scholar 

  31. Zeki S, Shipp S (1988) Nature 335:311

    Article  Google Scholar 

  32. Geman D, Jedynak B (1996) An active testing model for tracking roads in satellite images. Tech. Rep. 2757, INRIA

    Google Scholar 

  33. Amit Y, Geman D (1997) Neural Comput 9(7):1545

    Article  Google Scholar 

  34. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features

    Google Scholar 

  35. Lee H, Grosse R, Ranganath R, Ng AY (2009) In: International conference on machine learning

    Google Scholar 

  36. Ullman S (1994) In: Koch C, Davis J (eds) Large-scale neuronal theories of the brain. MIT Press, Cambridge, pp 257–270

    Google Scholar 

  37. Ullman S, Vidal-Naquet M, Sali E (2002) Nat Neurosci 5:682

    Google Scholar 

  38. Pelillo M, Siddiqi K, Zucker SW (1998) IEEE Trans Pattern Anal Mach Intell 21:1105

    Article  Google Scholar 

  39. Sivic J, Russell BC, Zisserman A, Freeman WT, Efros AA (2008) In: IEEE conference on computer vision and pattern recognition

    Google Scholar 

  40. Tappen MF, Freeman WT (2003) In: International conference on computer vision

    Google Scholar 

  41. Arora S (1998) J ACM 45(5):753. doi:10.1145/290179.290180

    Article  MathSciNet  MATH  Google Scholar 

  42. Arbeláez P, Maire M, Fowlkes C, Malik J (2011) IEEE Trans Pattern Anal Mach Intell 33(5):898. doi:10.1109/TPAMI.2010.161

    Article  Google Scholar 

  43. Ullman S, Epshtein B (2006) In: Ponce J, Hebert M, Schmid C, Zisserman A (eds) Toward category-level object recognition. Lecture notes in computer science, vol 4170. Springer, Berlin, pp 321–344

    Chapter  Google Scholar 

  44. Ben-Shahar O, Zucker SW (2003) Neural Comput 16:445

    Article  Google Scholar 

  45. Ben-Shahar O, Huggins P, Izo T, Zucker SW (2003) J Physiol (Paris) 97:191

    Article  Google Scholar 

  46. Ben-Shahar O, Zucker SW (2004) Neural Netw 17:753

    Article  Google Scholar 

  47. Kanizsa G (1979) Organization in vision: essays on Gestalt perception. Praeger, New York

    Google Scholar 

  48. Koffka K (1935) Principles of Gestalt psychology. Harcourt Brace and Co., New York

    Google Scholar 

  49. Zhou H, Friedman H, von der Heydt R (2000) J Neurosci 20:6594

    Google Scholar 

  50. Zucker SW (2012) J Physiol (Paris) 106:297

    Article  Google Scholar 

  51. Dimitrov P, Lawlor M, Zucker SW (2011) In: Third international conference on scale space and variational methods in computer vision, Israel

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Yale University, New Haven, CT, USA

    Steven W. Zucker

Authors
  1. Steven W. Zucker
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Steven W. Zucker .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of Toronto, King's College Road 6, Toronto, M5S 3G4, Ontario, Canada

    Sven J. Dickinson

  2. Department of Psychological Sciences, Purdue University, Third Street 703, West Lafayette, 47907, Indiana, USA

    Zygmunt Pizlo

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Zucker, S.W. (2013). The Visual Hierarchy Mirage: Seeing Trees in a Graph. In: Dickinson, S., Pizlo, Z. (eds) Shape Perception in Human and Computer Vision. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5195-1_11

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5195-1_11

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5194-4

  • Online ISBN: 978-1-4471-5195-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation