Purposive Evolution for Object Recognition Using an Artificial Visual Cortex

  • Eddie Clemente
  • Gustavo Olague
  • Leon Dozal
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 175)


This work presents a novel approach to synthesize an artificial visual cortex based on what we call organic genetic programming. Primate brains have several distinctive features that help in the outstanding display of perception achieved by the visual system, including binocular vision, memory, learning, and recognition, to mention but a few. These features are processed by a complex arrangement of highly interconnected and numerous cortical visual areas. This paper describes a system composed of an artificial dorsal pathway, or where stream, and an artificial ventral pathway, or what stream, that are fused to create a kind of artificial visual cortex. The idea is to show that genetic programming is able to evolve a high number of heterogeneous trees thanks to the hierarchical structure of our virtual brain. Thus, the proposal uses two key ideas: 1) the recognition of objects can be achieved by a hierarchical structure using the concept of function composition, 2) the evolved functions can be related to the tissues of an artificial organ. Experimental results provide evidence that high recognition rates could be achieved for a well-known multiclass object recognition problem.


Visual Cortex Genetic Programming Object Recognition Visual Attention Dorsal Stream 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Ayala, F.J.: Teleological explanations in evolutionary biology. Philosophy of Science 37(1), 1–15 (1970)MathSciNetCrossRefGoogle Scholar
  2. 2.
    Barton, R.A.: Visual specialization and brain evolution in primates. Proceedings of the Royal Society of London Series B-Biological Sciences 265(1409), 1933–1937 (1998)CrossRefGoogle Scholar
  3. 3.
    Chang, C.-C., Lin, C.-J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at Google Scholar
  4. 4.
    Clemente, E., Olague, G., Dozal, L., Mancilla, M.: Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming. In: Di Chio, C., Agapitos, A., Cagnoni, S., Cotta, C., de Vega, F.F., Di Caro, G.A., Drechsler, R., Ekárt, A., Esparcia-Alcázar, A.I., Farooq, M., Langdon, W.B., Merelo-Guervós, J.J., Preuss, M., Richter, H., Silva, S., Simões, A., Squillero, G., Tarantino, E., Tettamanzi, A.G.B., Togelius, J., Urquhart, N., Uyar, A.Ş., Yannakakis, G.N. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 315–325. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  5. 5.
    Creem, S.H., Proffitt, D.R.: Defining the cortical visual systems: “what”, “where”, and “how”. Acta Psychologica 107, 43–68 (2001)CrossRefGoogle Scholar
  6. 6.
    Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Annu. Rev. Neurosci., 193–222Google Scholar
  7. 7.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories, p. 178 (2004)Google Scholar
  8. 8.
    Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36, 193–202 (1980)MathSciNetzbMATHCrossRefGoogle Scholar
  9. 9.
    Hubel, D.H.: Exploration of the primary visual cortex. Nature, 515–524 (1982)Google Scholar
  10. 10.
    Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1953)Google Scholar
  11. 11.
    Itti, L., Koch, C.: Computational modelling of visual attention. Nature Review Neuroscience 2(3), 194–203 (2001)CrossRefGoogle Scholar
  12. 12.
    Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985)Google Scholar
  13. 13.
    Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)CrossRefGoogle Scholar
  14. 14.
    Lennox, J.G.: Darwin was a teleologist. Biology and Philosophy 8(4), 409–421 (1993)CrossRefGoogle Scholar
  15. 15.
    Mel, B.W.: Seemore: Combining color, shape, and texture histogramming in a neurally-inspired approach to visual object recognition. Neural Computation 9, 777–804 (1997)CrossRefGoogle Scholar
  16. 16.
    Milanese, R.: Detecting salient regions in an image: from biological evidence to computer implementation. PhD thesis, Department of Computer Science, University of Genova, Switzerland (December 1993)Google Scholar
  17. 17.
    Milner, A.D., Goodale, M.A.: The Visual Brain in Action, 2nd edn. Oxford University Press, Oxford (2006)CrossRefGoogle Scholar
  18. 18.
    Mutch, J., Lowe, D.G.: Object class recognition and localization using sparse features with limited receptive fields. Int. J. Comput. Vision 80, 45–57 (2008)CrossRefGoogle Scholar
  19. 19.
    Oram, M.W., Perrett, D.I.: Modeling visual recognition from neurobiological constraints. Neural Networks 7(6), 945–972 (1994)CrossRefGoogle Scholar
  20. 20.
    Rensink, R.A.: The dynamic representation of scenes. Visual Cognition 7(1-3), 17–42 (2000)CrossRefGoogle Scholar
  21. 21.
    Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience (11), 1019–1025Google Scholar
  22. 22.
    Schneider, G.E.: Contrasting visuomotor functions of tectum and cortex in the golden hamster. Psychologische Forschung 31(1), 52–62Google Scholar
  23. 23.
    Schneider, G.E.: Two visual systems. Science 163(3870), 895–902 (1969)CrossRefGoogle Scholar
  24. 24.
    Serre, T., Kouh, C., Cadieu, M., Knoblich, G., Kreiman, U., Poggio, T.: A theory of object recognition: Computations and circuits in the feedforward path of the ventral stream in primate visual cortex. Technical report, Massachusetts Institute of Technology Computer Science and Artificial Intelligence Laboratory, CBCL-259 (2005)Google Scholar
  25. 25.
    Short, T.: Darwin’s concept of final cause: neither new nor trivial. Biology and Philosophy 17, 323–340 (2002), doi:10.1023/A:1020173708395CrossRefGoogle Scholar
  26. 26.
    Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)CrossRefGoogle Scholar
  27. 27.
    Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nature Neuroscience 5(7), 682–687 (2002)Google Scholar
  28. 28.
    Ungerleider, L.G., Haxby, J.V.: “What” and “where” in the human brain. Current Opinion in Neurobiology 4(2), 157–165 (1994)CrossRefGoogle Scholar
  29. 29.
    Ungerleider, L.G., Mishkin, M.: Two Cortical Visual Systems, ch. 18, pp. 549–586 (1982)Google Scholar
  30. 30.
    Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19(9), 1395–1407 (2006)zbMATHCrossRefGoogle Scholar
  31. 31.
    Wolfe, J.M.: Visual Attention, 2nd edn., ch. 8, pp. 335–386. Academic Press (2000)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Tecnológico de Estudios Superiores de EcatepecEcatepec de MorelosMexico
  2. 2.EvoVision Project, Applied Physics DivisionCICESEMexicoMexico
  3. 3.EvoVision Project, Computer Science DepartmentCICESEMexicoMexico

Personalised recommendations