Biological Cybernetics

, 100:59 | Cite as

Computational object recognition: a biologically motivated approach

  • Tim C. Kietzmann
  • Sascha Lange
  • Martin Riedmiller
Original Paper


We propose a conceptual framework for artificial object recognition systems based on findings from neurophysiological and neuropsychological research on the visual system in primate cortex. We identify some essential questions, which have to be addressed in the course of designing object recognition systems. As answers, we review some major aspects of biological object recognition, which are then translated into the technical field of computer vision. The key suggestions are the use of incremental and view-based approaches together with the ability of online feature selection and the interconnection of object-views to form an overall object representation. The effectiveness of the computational approach is estimated by testing a possible realization in various tasks and conditions explicitly designed to allow for a direct comparison with the biological counterpart. The results exhibit excellent performance with regard to recognition accuracy, the creation of sparse models and the selection of appropriate features.


Biologically inspired computer vision Object recognition View-based object representations Feature selection Incremental learning 


  1. Abbott L, Rolls E, Tovee M (1996) Representational capacity of face coding in monkeys. Cereb Cortex 6(3): 498–505PubMedCrossRefGoogle Scholar
  2. Adams R, Bischof L (1994) Seeded region growing. IEEE Trans Pattern Anal Mach Intell 16(6): 641–647CrossRefGoogle Scholar
  3. Ahissar M, Hochstein S (2004) The reverse hierarchy theory of visual perceptual learning. Trends Cogn Sci 8(10): 457–464PubMedCrossRefGoogle Scholar
  4. Bar M (2003) A cortical mechanism for triggering top-down facilitation in visual object recognition. J Cogn Neurosci 15(4): 600–609PubMedCrossRefGoogle Scholar
  5. Bichot N, Schall J, Thompson K (1996) Visual feature selectivity in frontal eye fields induced by experience in mature macaques. Nature 381(6584): 697–699PubMedCrossRefGoogle Scholar
  6. Biederman I (1986) Human image understanding: recent research and a theory. Papers from the second workshop, vol 13 on Human and Machine Vision II table of contents, pp 13–57Google Scholar
  7. Bishop C (1995) Neural networks for pattern recognition. Oxford University Press, Inc., New YorkGoogle Scholar
  8. Bojer T, Hammer B, Koers C (2003) Monitoring technical systems with prototype based clustering. European Symposium on Artificial Neural Networks, pp 433–439Google Scholar
  9. Booth M, Rolls E (1998) View-invariant representations of familiar objects by neurons in the inferior temporal visual cortex. Cereb Cortex 8(6): 510–523PubMedCrossRefGoogle Scholar
  10. Bradski G, Grossberg S (1995) Fast-learning VIEWNET architectures for recognizing three-dimensional objects from multiple two-dimensional views. Neural Netw 8(7): 1053–1080CrossRefGoogle Scholar
  11. Bülthoff H, Edelman S (1992) Psychophysical support for a two- dimensional view interpolation theory of object recognition. Proc Natl Acad Sci USA 89(1): 60–64PubMedCrossRefGoogle Scholar
  12. Chun M, Marois R (2002) The dark side of visual attention. Curr Opin Neurobiol 12(2): 184–189PubMedCrossRefGoogle Scholar
  13. Edelman S, Weinshall D (1991) A self-organizing multiple-view representation of 3d objects. Biol Cybern 64(3): 209–219PubMedCrossRefGoogle Scholar
  14. Einhäuser W, Hipp J, Eggert J, Körner E, König P (2005) Learning viewpoint invariant object representations using a temporal coherence principle. Biol Cybern 93(1): 79–90PubMedCrossRefGoogle Scholar
  15. Erickson C, Desimone R (1999) Responses of macaque perirhinal neurons during and after visual stimulus association learning. J Neurosci 19(23): 10404PubMedGoogle Scholar
  16. Goldstone R (1998) Perceptual learning. Ann Rev Psychol 49Google Scholar
  17. Goodale M (1993) Visual pathways supporting perception and action in the primate cerebral cortex. Curr Opin Neurobiol 3(4): 578–585PubMedCrossRefGoogle Scholar
  18. Haider H, Frensch P (1996) The role of information reduction in skill acquisition. Cognit Psychol 30(3): 304–337PubMedCrossRefGoogle Scholar
  19. Hu M (1962) Visual pattern recognition by moment invariants. IEEE Trans Inf Theory 8(2): 179–187CrossRefGoogle Scholar
  20. Jagadeesh B, Chelazzi L, Mishkin M, Desimone R (2001) Learning increases stimulus salience in anterior inferior temporal cortex of the macaque. J Neurophysiol 86(1): 290–303PubMedGoogle Scholar
  21. Jugessur D, Dudek G (2000) Local appearance for robust object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, vol 1, 2000Google Scholar
  22. Kietzmann TC, Lange S, Riedmiller M (2008) Incremental GRLVQ: Learning relevant features for 3D object recognition. Neurocomputing 71: 2868–2879CrossRefGoogle Scholar
  23. Kirstein S, Wersing H, Korner E (2005) Rapid online learning of objects in a biologically motivated recognition architecture. 27th Pattern Recognition Symposium DAGM, pp 301–308Google Scholar
  24. Kobatake E, Tanaka K (1994) Neuronal selectivities to complex object features in the ventral visual pathway of the macaque cerebral cortex. J Neurophysiol 71(3): 856–867PubMedGoogle Scholar
  25. Kobatake E, Wang G, Tanaka K (1998) Effects of shape-discrimination training on the selectivity of inferotemporal cells in adult monkeys. J Neurophysiol 80(1): 324–330PubMedGoogle Scholar
  26. Koenderink J, Doorn A (1979) The internal representation of solid shape with respect to vision. Biol Cybern 32(4): 211–216PubMedCrossRefGoogle Scholar
  27. Lange S, Riedmiller M (2006) Appearance based robot discrimination using eigenimages. In: Nardi D, Riedmiller M, Sammut C, Santos-Victor J (eds) RoboCup-2004: Robot Soccer World Cup VIII. Springer, LCNS, Berlin 2005Google Scholar
  28. Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11): 2278–2324CrossRefGoogle Scholar
  29. Leibe B, Schiele B (2003) Analyzing appearance and contour based methods for object categorization. Proc IEEE Conf Comput Vis Pattern Recognit (CVPR’03)Google Scholar
  30. Logothetis N, Pauls J, Poggio T (1995) Shape representation in the inferior temporal cortex of monkeys. Curr Biol 5(5): 552–563PubMedCrossRefGoogle Scholar
  31. Lowe D (1985) Perceptual Organization and Visual Recognition. Kluwer Academic Publishers, NorwellGoogle Scholar
  32. Lowe D (1999) Object recognition from local scale-invariant features. In: The Proceedings of the Seventh IEEE International Conference on Computer Vision,vol 2Google Scholar
  33. Lowe D (2000) Towards a computational model for object recognition in it cortex. Biol Motiv Comput Vis 1811: 20–31Google Scholar
  34. Luong Chi M (2006) Introduction To Computer Vision and Computer Graphics. Institute of Information Technology, Hanoi, VietnamGoogle Scholar
  35. Mareschal D, Plunkett K, Harris P (1999) A computational and neuropsychological account of object-oriented behaviours in infancy. Dev Sci 2(3): 306–317CrossRefGoogle Scholar
  36. Marr D, Nishihara H (1978) Representation and Recognition of the Spatial Organization of Three-Dimensional Shapes. Proc R Soc Lond Ser B Biol Sci 200(1140): 269–294CrossRefGoogle Scholar
  37. Massad A, Mertsching B, Schmalz S (1998) Combining multiple views and temporal associations for 3-d object recognition. Proc ECCV 98: 699–715Google Scholar
  38. Maunsell J, Treue S (2006) Feature-based attention in visual cortex. Trends Neurosci 29(6): 317–322PubMedCrossRefGoogle Scholar
  39. Mel B (1997) SEEMORE: Combining color, shape, and texture histogramming in a neurally-inspired approach to visual object recognition. Neural Comput 9(4): 777–804PubMedCrossRefGoogle Scholar
  40. Milner A, Goodale M (1993) Visual pathways to perception and action. Prog Brain Res 95: 317–337PubMedCrossRefGoogle Scholar
  41. Milner A, Goodale M (1996) The visual brain in action. Oxford University Press, NYGoogle Scholar
  42. Miyashita Y (1988) Neuronal correlate of visual associative long-term memory in the primate temporal cortex. Nature 335(6193): 817–820PubMedCrossRefGoogle Scholar
  43. Miyashita Y (1993) Inferior temporal cortex: Where visual perception meets memory. Ann Rev Neurosci 16(1): 245–263PubMedCrossRefGoogle Scholar
  44. Murphy-Chutorian E, Aboutalib S, Triesch J (2005) Analysis of a biologically-inspired system for real-time object recognition. Cognit Sci Online 3(2): 1–14Google Scholar
  45. Murray S, Wojciulik E (2004) Attention increases neural selectivity in the human lateral occipital complex. Nature Neurosci 7: 70–74PubMedCrossRefGoogle Scholar
  46. Mutch J, Lowe D (2006) Multiclass object recognition with sparse, localized features. In: Proceedings of the 2006 IEEE computer society conference on computer vision and pattern recognition, vol 1, pp 11–18Google Scholar
  47. Mutch J, Lowe D (2007) Object class recognition and localization using sparse features with limited receptive fields. In IJCVGoogle Scholar
  48. Nene S, Nayar S, Murase H (1996) Columbia object image library (COIL-100). Techn. Rep. No. CUCS-006-96, dept. Comp. Science, Columbia UniversityGoogle Scholar
  49. Nosofsky R (1984) Attention, Similarity, and the Identification-Categorization Relationship. Dissertation, Harvard UniversityGoogle Scholar
  50. Obdrzalek S, Matas J (2002) Object recognition using local affine frames on distinguished regions. BMVC 2002, pp 113–122Google Scholar
  51. Oliva A (2005) Gist of a scene. Neurobiology of attention, pp 251–256Google Scholar
  52. Paletta L, Pinz A (2000) Active object recognition by view integration and reinforcement learning. Rob Auton Syst 31(1-2): 71–86CrossRefGoogle Scholar
  53. Palmer S, Rosch E, Chase P (1981) Canonical perspective and the perception of objects. Attention and performance IX, pp 135–151Google Scholar
  54. Perrett D, Hietanen J, Oram M, Benson P, Rolls E (1992) Organization and functions of cells responsive to faces in the temporal cortex. Philos Trans Biol Sci 335(1273): 23–30CrossRefGoogle Scholar
  55. Perrett D, Mistlin A, Chitty A (1987) Visual cells responsive to faces. Trends Neurosci 10: 358–364CrossRefGoogle Scholar
  56. Perrett D, Oram M, Ashbridge E (1998) Evidence accumulation in cell populations responsive to faces: An account of generalization of recognition without mental transformations. Cognition 67: 111–145PubMedCrossRefGoogle Scholar
  57. Perrett D, Oram M, Harries M, Bevan R, Benson P, Thomas S (1991) Viewer centered and object centered coding of heads in the macaque temporal cortex. Exp Brain Res 86: 159–173PubMedCrossRefGoogle Scholar
  58. Poggio T, Edelman S (1990) A network that learns to recognize three-dimensional objects. Nature 343: 263–266 34PubMedCrossRefGoogle Scholar
  59. Rao R (1997) Dynamic appearance-based recognition. In: Proceedings of computer vision and pattern recognitionGoogle Scholar
  60. Riesenhuber M, Poggio T (1999) Hierarchical models of object recognition in cortex. Nat Neurosci 2: 1019–1025PubMedCrossRefGoogle Scholar
  61. Riesenhuber M, Poggio T (2000) Models of object recognition. Nat Neurosci 3: 1199–1204PubMedCrossRefGoogle Scholar
  62. Riesenhuber M, Poggio T (2003) How visual cortex recognizes objects: The tale of the standard model. Vis Neurosci 2: 1640–1653Google Scholar
  63. Roobaert D, Van Hulle M (1999) View-based 3d object recognition with support vector machines. Neural Networks for Signal Processing IX, 1999. In: Proceedings of the 1999 IEEE Signal Processing Society Workshop, pp 77–84Google Scholar
  64. Sakai K, Miyashita Y (1991) Neural organization for the long-term memory of paired associates. Nature 354(6349): 152–155PubMedCrossRefGoogle Scholar
  65. Schneider G, Wersing H, Sendhoff B, Korner E, Schneider G, Wersing H (2004) Evolution of hierarchical features for visual object recognition. Third Workshop on SelfOrganization of AdaptiVE Behavior (SOAVE 2004) Ilmenau, pp 104–113Google Scholar
  66. Seibert M, Waxman A (1992) Adaptive 3-d object recognition from multiple views. IEEE Trans Pattern Anal Mach Intell 14(2): 107–124CrossRefGoogle Scholar
  67. Serre T, Wolf L, Poggio T (2005) Object recognition with features inspired by visual cortex. In: IEEE Computer society conference on Computer vision and pattern recognition, 2005. CVPR 2005, vol 2Google Scholar
  68. Shokoufandeh A, Marsic I, Dickinson S (1999) View-based object recognition using saliency maps. Image Vis Comput 17(5): 445–460CrossRefGoogle Scholar
  69. Strickert M, Bojer T, Hammer B (2001) Generalized relevance LVQ for time series. Springer, Berlin, pp 677–683Google Scholar
  70. Suard F, Rakotomamonjy A, Bensrhair A (2006) Object categorization using kernels combining graphs and histograms of gradients. In: International conference on image analysis and recognition, vol 2, pp 23–34Google Scholar
  71. Tanaka K (1992) Inferotemporal cortex and higher visual functions. Curr Opin Neurobiol 2(4): 502–505PubMedCrossRefGoogle Scholar
  72. Tanaka K (1996) Inferotemporal cortex and object vision. Ann Rev Neurosci 19(1): 109–139PubMedCrossRefGoogle Scholar
  73. Tarr M, Bülthoff H (1995) Is human object recognition better described by geon-structural-descriptions or by multiple-views. J Exp Psychol Human Percept Perform 21(6): 1494–1505CrossRefGoogle Scholar
  74. Tarr M, Bülthoff H (1998) Image-based object recognition in man, monkey and machine. Cognition 67(1): 1–20PubMedCrossRefGoogle Scholar
  75. Tarr M, Pinker S (1989) Mental rotation and orientation-dependence in shape recognition. Cognit Psychol 21(2): 233–282PubMedCrossRefGoogle Scholar
  76. Teynor A, Rahtu E, Setia L, Burkhardt H, Teynor A, Rahtu E, Setia L, Burkhardt H (2006) Properties of patch based approaches for the recognition of visual object classes. In: Pattern recognition, DAGM 2006 Proceedings, lecture notes in computer science, vol 4174, pp 284–293Google Scholar
  77. Thompson D, Mundy J (1987) Three-dimensional model matching from an unconstrained viewpoint. In: Proceedings of the IEEE International Conference on Robotics and Automation, vol 4Google Scholar
  78. Tuytelaars T, Van Gool L et al (1999) Content-based image retrieval based on local affinely invariant regions. International conference on visual information systems, pp 493–500Google Scholar
  79. Ullman S, Basri R (1991) Recognition by linear combinations of models. IEEE Trans Pattern Anal Mach Intell 13(10): 992–1006CrossRefGoogle Scholar
  80. Ullman S, Vidal-Naquet M, Sali E (2002) Visual features of intermediate complexity and their use in classification. Nat Neurosci 5: 682–687PubMedGoogle Scholar
  81. Voigtländer A, Lange S, Lauer M, Riedmiller M (2007) Real-time 3d ball recognition using perspective and catadioptric cameras. In ECMR 2007Google Scholar
  82. Vuilleumier P, Henson R, Driver J, Dolan R (2002) Multiple levels of visual object constancy revealed by event-related fMRI of repetition priming. Nat Neurosci 5(5): 491–499PubMedCrossRefGoogle Scholar
  83. Wallis G (1996) How neurons learn to associate 2d-views in invariant object recognition. Technical report, Technical Report NoGoogle Scholar
  84. Wallis G (1998) Temporal order in human object recognition learning. J Biol Syst 6(3): 299–313CrossRefGoogle Scholar
  85. Wallis G, Bülthoff H (1999) Learning to recognize objects. Trends Cognit Sci 3(1): 22–31CrossRefGoogle Scholar
  86. Wallis G, Bülthoff H (2001) Effects of temporal association on recognition memory. Proc Natl Acad Sci 98(8): 4800–4804PubMedCrossRefGoogle Scholar
  87. Wallraven C, Bülthoff H (2001a) Automatic acquisition of exemplar-based representations for recognition from image sequences. In: Proceedings of the CVPR’01-workshop models versus exemplars, vol 28Google Scholar
  88. Wallraven C, Bülthoff H (2001b) View-based recognition under illumination changes using local features. In: Proceedings of the CVPR’01-workshop on identifying objects across variations in lighting: psychophysics and computation, vol 3Google Scholar
  89. Walther D, Fei-Fei L (2007) Task-set switching with natural scenes: Measuring the cost of deploying top-down attention. J Vis 7(11): 9PubMedCrossRefGoogle Scholar
  90. Wersing H, Korner E (2002) Unsupervised learning of combination features for hierarchical recognition models. International conference on artificial neural network, ICANN. 11Google Scholar
  91. Würtz R (1995) Multilayer dynamic link networks for establishing image point correspondences and visual object recognition. Verlag Harri DeutschGoogle Scholar
  92. Young M, Yamane S (1992) Sparse population coding of faces in the inferotemporal cortex. Science 256(5061): 1327PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Tim C. Kietzmann
    • 1
  • Sascha Lange
    • 2
  • Martin Riedmiller
    • 2
  1. 1.Institute of Cognitive ScienceUniversity of OsnabrückOsnabrückGermany
  2. 2.Institute of Computer ScienceUniversity of OsnabrückOsnabrückGermany

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