Brain Programming and the Random Search in Object Categorization

  • Gustavo Olague
  • Eddie Clemente
  • Daniel E. Hernández
  • Aaron Barrera
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10199)

Abstract

Computational neuroscience lays the foundations of intelligent behavior through the application of machine learning approaches. Brain programming, which derives from such approaches, is emerging as a new evolutionary computing paradigm for solving computer vision and pattern recognition problems. Primate brains have several distinctive features that are obtained by a complex arrangement of highly interconnected and numerous cortical visual areas. This paper describes a virtual system that mimics the complex structure of primate brains composed of an artificial dorsal pathway – or “where” stream – and an artificial ventral pathway – or “what” stream – that are fused to recreate an artificial visual cortex. The goal is to show that brain programming is able to discover numerous heterogeneous functions that are applied within a hierarchical structure of our virtual brain. Thus, the proposal applies two key ideas: first, object recognition can be achieved by a hierarchical structure in combination with the concept of function composition; second, the functions can be discovered through multiple random runs of the search process. This last point is important since is the first step in any evolutionary algorithm; in this way, enhancing the possibilities for solving hard optimization problems.

Keywords

Object recognition Random search Brain programming 
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Notes

Acknowledgments

This research was founded by CONACyT through the Project 155045 - “Programación cerebral aplicada al estudio del pensamiento y la visión”. This work is also supported by ITE-TecNM through the project 5748.16-P, “Optimización de controladores aplicados a la navegación de un robot móvil, utilizando cómputo evolutivo”.

References

  1. 1.
    Olague, G.: Evolutionary Computer Vision: The First Footprints. Springer, Heidelberg (2016)CrossRefGoogle Scholar
  2. 2.
    Logothetis, N.K., Sheinberg, D.L.: Visual object recognition. Ann. Rev. Neurosci. 19, 577–621 (1996)CrossRefGoogle Scholar
  3. 3.
    DiCarlo, J.J., Zoccolan, D., Rust, N.C.: How does the brain solve visual object recognition? Neuron 73(3), 415–434 (2012)CrossRefGoogle Scholar
  4. 4.
    Riesenhuber, M., Poggio, T.: Models of object recognition. Nat. Neurosci. 3, 1199–1204 (2000)CrossRefGoogle Scholar
  5. 5.
    Rees, G., Frackowiak, R., Frith, C.: Two modulatory effects of attention that mediate object categorization in human cortex. Science. 275(5301), 835–8 (1997)CrossRefGoogle Scholar
  6. 6.
    Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Ann. Rev. Neurosci. 18, 193–222 (1995)CrossRefGoogle Scholar
  7. 7.
    Kastner, S., Ungerleider, L.G.: Mechanisms of visual attention in the human cortex. Ann. Rev. Neurosci. 23, 315–341 (2000)CrossRefGoogle Scholar
  8. 8.
    Milner, A.D., Goodale, M.A.: The Visual Brain in Action, 2nd edn. Oxford University Press, Oxford (2006)CrossRefGoogle Scholar
  9. 9.
    Creem, S.H., Proffitt, D.R.: Defining the cortical visual systems: “what”, “where”, and “how”. Acta Psychol. 107(1–3), 43–68 (2001)CrossRefGoogle Scholar
  10. 10.
    Farivar, R.: Dorsal-ventral integration in object recognition. Brain Res. Rev. 61(2), 144–153 (2009)CrossRefGoogle Scholar
  11. 11.
    Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)MathSciNetCrossRefMATHGoogle Scholar
  12. 12.
    Serre, T., Kouh, C., Cadieu, M., Knoblich, G., Kreiman, U., Poggio, T.: 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 (2005)Google Scholar
  13. 13.
    Mutch, J., Lowe, D.G.: Object class recognition and localization using sparse features with limited receptive fields. Int. J. Comput. Vis. 80(1), 45–57 (2008)CrossRefGoogle Scholar
  14. 14.
    Mel, B.W.: Seemore: combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition. Neural Comput. 9(4), 777–804 (1997)CrossRefGoogle Scholar
  15. 15.
    Itti, L., Koch, C.: Computational modeling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001)CrossRefGoogle Scholar
  16. 16.
    Clemente, E., Olague, G., Dozal, L., Mancilla, M.: Object recognition with an optimized ventral stream model using genetic programming. In: Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 315–325. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-29178-4_32CrossRefGoogle Scholar
  17. 17.
    Clemente, E., Olague, G., Dozal, L.: Purposive evolution for object recognition using an artificial visual cortex. In: Schuetze, O., Coello, C.A.C., Tantar, A.-A., Tantar, E., Bouvry, P., Del Moral, P., Legrand, P. (eds.) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation II, pp. 355–370. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  18. 18.
    Olague, G., Clemente, E., Dozal, L., Hernádez, D.E.: Evolving an artificial visual cortex for object recognition with brain programming. In: Schuetze, O., Coello, C.A.C., Tantar, A.-A., Tantar, E., Bouvry, P., Del Moral, P., Legrand, P., et al. (eds.) EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. Springer, Heidelberg (2014)Google Scholar
  19. 19.
    Dozal, L., Olague, G., Clemente, E., Hernandez, D.E.: Brain programming for the evolution of an articial dorsal stream. Cognit. Comput. 6(3), 528–557 (2014)CrossRefGoogle Scholar
  20. 20.
    Hernandez, D.E., Clemente, E., Olague, G., Briseño, J.L.: Evolutionary multi-objective visual cortex for object classification in natural images. J. Comput. Sci. 17(1), 216–233 (2016)Google Scholar
  21. 21.
    Clemente, E., Chavez, F., Fernandez de Vega, F., Olague, G.: Self-adjusting focus of attention in combination with a genetic fuzzy system for improving a laser environment control device system. Appl. Soft Comput. 32, 250–265 (2015)CrossRefGoogle Scholar
  22. 22.
    Fukushima, K.: Neural network model for selective attention in visual pattern recognition and associative recall. Appl. Opt. 26(23), 4985–4992 (1987)CrossRefGoogle Scholar
  23. 23.
    Olshausen, B.A., Anderson, C.H., Van Essen, D.C.: A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. J. Neurosci. 13(11), 4700–4719 (1993)CrossRefGoogle Scholar
  24. 24.
    Walther, D., Itti, L., Riesenhuber, M., Poggio, T., Koch, C.: Attentional selection for object recognition — a gentle way. In: Bülthoff, H.H., Wallraven, C., Lee, S.-W., Poggio, T.A. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 472–479. Springer, Heidelberg (2002). doi: 10.1007/3-540-36181-2_47CrossRefGoogle Scholar
  25. 25.
    Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2(11), 1019–1025 (1999)CrossRefGoogle Scholar
  26. 26.
    Walther, D., Koch, C.: Attention in hierarchical models of object recognition. Progr. Brain Res. 165, 57–78 (2007)CrossRefGoogle Scholar
  27. 27.
    Heinke, D., Humphteys, G.W.: Attention, spatial representation, and visual neglect: simulating emergent attention and spatial memory in the selective attention for identification model (SAIM). Psychol. Rev. 110(1), 29–87 (2003)CrossRefGoogle Scholar
  28. 28.
    Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980)CrossRefGoogle Scholar
  29. 29.
    Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Netw. 19(9), 1395–407 (2006)CrossRefMATHGoogle Scholar
  30. 30.
    Pinto, N., Cox, D.D., DiCarlo, J.J.: Why is real-world visual object recognition hard? PLoS Comput. Biol. 4(1), 151–156 (2008)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Ponce, J., et al.: Dataset issues in object recognition. In: Ponce, J., Hebert, M., Schmid, C., Zisserman, A. (eds.) Toward Category-Level Object Recognition. LNCS, vol. 4170, pp. 29–48. Springer, Heidelberg (2006). doi: 10.1007/11957959_2CrossRefGoogle Scholar
  32. 32.
    Wang, Z., Feng, J.: Multi-class learning from class proportions. Neurocomputing 119, 273–280 (2013)CrossRefGoogle Scholar
  33. 33.
    Ji, Z., Wang, J., Su, Y., Song, Z., Xing, S.: Balance between object and background: object-enhanced features for scene image classification. Neurocomputing 120, 15–23 (2013)CrossRefGoogle Scholar
  34. 34.
    Chen, B., Polatkan, G., Sapiro, G., Blei, D., Dunson, D., Carin, L.: Deep learning with hierarchical convolutional factor analysis. IEEE Trans. Pattern Anal. Mach. Intell. 8(35), 1887–1901 (2013)CrossRefGoogle Scholar
  35. 35.
    Xu, B., Hu, R., Guo, P.: Combining affinity propagation with supervised dictionary learning for image classification. Neural Comput. Appl. 22(7–8), 1301–1308 (2013)CrossRefGoogle Scholar
  36. 36.
    Chandra, S., Kumar, S., Jawahar, C.V.: Learning hierarchical bag of words using naive bayes clustering. In: Lee, K.M., Matsushita, Y., Rehg, J.M., Hu, Z. (eds.) ACCV 2012. LNCS, vol. 7724, pp. 382–395. Springer, Heidelberg (2013). doi: 10.1007/978-3-642-37331-2_29CrossRefGoogle Scholar
  37. 37.
    Wilcoxon, F.: Individual comparison by ranking methods. Biometr. Bull. 1(6), 80–83 (1945)CrossRefGoogle Scholar
  38. 38.
    Massey, F.J.: The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46(253), 68–78 (1951)CrossRefMATHGoogle Scholar
  39. 39.
    Hernandez, D.E., Olague, G., Hernandez, B., Clemente, E.: CUDA-based parallelization of a bio-inspired model for fast object classification. Neural Comput. Appl. (2017). doi: 10.1007/s00521-017-2873-3

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Gustavo Olague
    • 1
  • Eddie Clemente
    • 2
  • Daniel E. Hernández
    • 3
  • Aaron Barrera
    • 1
  1. 1.Centro de Investigación Científica y de Educación Superior de EnsenadaEnsenadaMexico
  2. 2.Instituto Tecnológico de EnsenadaEnsenadaMexico
  3. 3.Instituto Tecnológico de TijuanaTijuanaMexico

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