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Part-Based Strategies for Visual Categorisation and Object Recognition

  • Martin Jüttner

Abstract

Approaches to object recognition that rely on structural, or part-based, descriptions have a long-standing tradition in research on both computer and biological vision. Originally developed in the field of computer graphics, Binford (1971) was among the first to suggest that similar representations might be used by biological systems for object recognition. According to this author, such representations could be based on certain three-dimensional (3D) part primitives termed “generalized cones”.

Keywords

Object Recognition Category Learning Representational Format Binary Attribute General Recognition Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Ashby FG (1989) Stochastic general recognition theory. In: Vickers D, Smith PL (Eds) Human information processing: measures, mechanisms, and models. Elsevier, Amsterdam, pp 435–457Google Scholar
  2. Biederman I (1972) Perceiving real-world scenes. Science 177:77–80PubMedCrossRefGoogle Scholar
  3. Biederman I (1981) On the semantics of a glance at a scene. In: Kubovy M, Pomerantz RJ (Eds) Perceptual organization. Erlbaum, Hillsdale NJ, pp 213–253Google Scholar
  4. Biederman I (1987) Recognition-by-components: a theory of human image understanding. Psychol Rev 94:115–147PubMedCrossRefGoogle Scholar
  5. Binford T (1971) Visual perception by computer. Proceedings, IEEE conference on systems and control. Miami, FLGoogle Scholar
  6. Bischof WF, Caelli T (1997) SURE: scene understanding by rule evaluation. IEEE Trans Patt Anal Machine Intell 19:1284–1288CrossRefGoogle Scholar
  7. Bunke H (2000) Graph matching for visual object recognition. Spat Vis 13:335–340PubMedCrossRefGoogle Scholar
  8. Caelli T, Dreier A (1994) Variations on the evidence-based object recognition theme. Pattern Recogn 27:1231–1248CrossRefGoogle Scholar
  9. Chun MM, Jiang Y (1998) Contextual cueing: implicit learning and memory of visual context guides spatial attention. Cogn Psychol 36:28–71PubMedCrossRefGoogle Scholar
  10. De Graef P, Christiaens D, d’Ydewalle G (1990) Perceptual effects of scene context on object identification. Psychol Res 52:317–329PubMedCrossRefGoogle Scholar
  11. Edelman S, Intrator N (2000) Coarse coding of shape fragments) + (retinotopy) ≈ representation of structure. Spat Vis 13:255–264PubMedCrossRefGoogle Scholar
  12. Fan T, Medioni R, Nevatia R (1989) Recognizing 3-D objects using surface descriptions. IEEE Trans Patt Anal Machine Intell 11:1140–1156CrossRefGoogle Scholar
  13. Farah MJ, Wilson KD, Drain M, Tanaka JW (1998) What is “special” about face perception? Psychol Rev 105:482–498PubMedCrossRefGoogle Scholar
  14. Foster DH, Gilson SJ (2002) Recognizing novel three-dimensional objects by summing signals from parts and views. Proc R Soc Lond B 269:1939–1947CrossRefGoogle Scholar
  15. Gauthier I, Tarr MJ (2002) Unraveling mechanisms for expert object recognition: bridging brain and behavior. J Exp Psychol Hum 28:431–446CrossRefGoogle Scholar
  16. Gross CG, Bornstein MH (1978) Left and right in science and art. Leonardo 11:29–38CrossRefGoogle Scholar
  17. Haywood WG (2003) After the viewpoint debate: where next in object recognition. Trends Cogn Sci 7:425–427CrossRefGoogle Scholar
  18. Henderson JM, Weeks PA, Hollingworth A (1999) The effects of semantic consistency on eye movements during complex viewing. J Exp Psychol Hum 25:210–228CrossRefGoogle Scholar
  19. Hummel JE (2001) Complementary solutions to the binding problem in vision: implications for shape perception and object recognition. Vis Cogn 8:489–517CrossRefGoogle Scholar
  20. Hummel JE, Biederman I (1992) Dynamic binding in a neural network for shape recognition. Psychol Rev 99:480–517PubMedCrossRefGoogle Scholar
  21. Jain AK, Hoffman R (1988) Evidence-based recognition of 3-D objects. IEEE Trans Patt Anal Machine Intell 10:783–802CrossRefGoogle Scholar
  22. Johnson KE, Mervis CB (1997) Effects of varying levels of expertise on the basic level of categorization. J Exp Psychol Gen 126:248–277PubMedCrossRefGoogle Scholar
  23. Jüttner M, Rentschler I (1996) Reduced perceptual dimensionality in extrafoveal vision. Vision Res 36:1007–1022PubMedCrossRefGoogle Scholar
  24. Jüttner M, Caelli T, Rentschler I (1997) Evidence-based pattern recognition: a structural approach to human perceptual learning and generalization. J Math Psychol 41:244–258CrossRefGoogle Scholar
  25. Jüttner M, Langguth B, Rentschler I (2004) The impact of context on pattern category learning and representation. Vis Cogn 11:921–945CrossRefGoogle Scholar
  26. Marr D (1976) Early processing of visual information. Philos T Roy Soc B 275:483–524CrossRefGoogle Scholar
  27. Marr D, Nishihara HK (1978) Representation and recognition of the spatial organization of three-dimensional shapes. Proc R Soc Lond B 200:269–294PubMedCrossRefGoogle Scholar
  28. Medin DL, Schaffer MM (1978) Context theory of classification learning. Psychol Rev 85:207–238CrossRefGoogle Scholar
  29. Morris RK (1994) Lexical and message level sentence context effects of fixation times in reading. J Exp Psychol Learn 20:92–103CrossRefGoogle Scholar
  30. Nosofsky RM (1986) Attention, similarity, and the identification-categorization relationship. J Exp Psychol Gen 115:39–57PubMedCrossRefGoogle Scholar
  31. Palmer SE (1975) The effects of contextual scenes on the identification of objects. Mem Cogn 3:519–526Google Scholar
  32. Pearce AR, Caelli T (1999) Interactively matching hand-drawings using induction. Comput Vis Image Underst 73:391–403CrossRefGoogle Scholar
  33. Pinker S (1984) Visual cognition: an introduction. Cognition 18:1–63PubMedCrossRefGoogle Scholar
  34. Poggio T, Edelman S (1990) A network that learns to recognize three-dimensional objects. Nature 343:263–266PubMedCrossRefGoogle Scholar
  35. Reed SK (1972) Pattern recognition and categorization. Cogn Psychol 3:382–407CrossRefGoogle Scholar
  36. Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in the cortex. Nat Neurosci 2:1019–1025PubMedCrossRefGoogle Scholar
  37. Rivlin E, Dickenson S, Rosenfeld A (1995) Recognition by functional parts. Comput Vis Image Underst 62:164–176CrossRefGoogle Scholar
  38. Rosch E (1978) Principles of categorization. In: Rosch E, Lloyd B (Eds) Cognition and categorization. Erlbaum, Hillsdale NJ, pp 27–48Google Scholar
  39. Shapiro L, Haralick R (1981) Structural descriptions and inexact matching. IEEE Trans Patt Anal Machine Intell 3:504–519CrossRefGoogle Scholar
  40. Stankiewicz BJ (2002) Empirical evidence for independent dimensions in the visual representation of three-dimensional shape. J Exp Psychol Hum 28:913–932CrossRefGoogle Scholar
  41. Tanaka JW, Taylor M (1991) Object categories and expertise: is the basic level in the eye of the beholder? Cogn Psychol 23:457–482CrossRefGoogle Scholar
  42. Thoma V, Hummel JE, Davidoff J (2004) Evidence for holistic representations of ignored images and analytic representations of attended images. J Exp Psychol Hum 30:257–267CrossRefGoogle Scholar
  43. Ullman S, Sali E (2000) Object classification using a fragment-based representation. In: Lee SW, Bülthoff HH (Eds) Biologically motivated computer vision. Springer, Berlin Heidelberg New York, pp 73–87Google Scholar
  44. Vuilleumier P, Henson RN, Driver J, Dolan RJ (2002) Multiple levels of visual object constancy revealed by event-related fMRI of repition priming. Nat Neurosci 5:491–499PubMedCrossRefGoogle Scholar
  45. Zetzsche C, Barth E (1990) Fundamental limits of linear filters in the visual processing of two-dimensional signals. Vision Res 30:1111–1117PubMedCrossRefGoogle Scholar

Copyright information

© Springer 2007

Authors and Affiliations

  • Martin Jüttner
    • 1
  1. 1.School of Life and Health Sciences — PsychologyAston UniversityAston Triangle, BirminghamUK

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