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European Conference on the Applications of Evolutionary Computation

EvoApplications 2017: Applications of Evolutionary Computation pp 522–537Cite as

Brain Programming and the Random Search in Object Categorization

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

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References

  1. Olague, G.: Evolutionary Computer Vision: The First Footprints. Springer, Heidelberg (2016)

    Book  Google Scholar 

  2. Logothetis, N.K., Sheinberg, D.L.: Visual object recognition. Ann. Rev. Neurosci. 19, 577–621 (1996)

    Article  Google Scholar 

  3. DiCarlo, J.J., Zoccolan, D., Rust, N.C.: How does the brain solve visual object recognition? Neuron 73(3), 415–434 (2012)

    Article  Google Scholar 

  4. Riesenhuber, M., Poggio, T.: Models of object recognition. Nat. Neurosci. 3, 1199–1204 (2000)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  6. Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Ann. Rev. Neurosci. 18, 193–222 (1995)

    Article  Google Scholar 

  7. Kastner, S., Ungerleider, L.G.: Mechanisms of visual attention in the human cortex. Ann. Rev. Neurosci. 23, 315–341 (2000)

    Article  Google Scholar 

  8. Milner, A.D., Goodale, M.A.: The Visual Brain in Action, 2nd edn. Oxford University Press, Oxford (2006)

    Book  Google Scholar 

  9. Creem, S.H., Proffitt, D.R.: Defining the cortical visual systems: “what”, “where”, and “how”. Acta Psychol. 107(1–3), 43–68 (2001)

    Article  Google Scholar 

  10. Farivar, R.: Dorsal-ventral integration in object recognition. Brain Res. Rev. 61(2), 144–153 (2009)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  15. Itti, L., Koch, C.: Computational modeling of visual attention. Nat. Rev. Neurosci. 2(3), 194–203 (2001)

    Article  Google Scholar 

  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_32

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  22. Fukushima, K.: Neural network model for selective attention in visual pattern recognition and associative recall. Appl. Opt. 26(23), 4985–4992 (1987)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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_47

    Chapter  Google Scholar 

  25. Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nat. Neurosci. 2(11), 1019–1025 (1999)

    Article  Google Scholar 

  26. Walther, D., Koch, C.: Attention in hierarchical models of object recognition. Progr. Brain Res. 165, 57–78 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  28. Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cogn. Psychol. 12(1), 97–136 (1980)

    Article  Google Scholar 

  29. Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Netw. 19(9), 1395–407 (2006)

    Article  MATH  Google Scholar 

  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)

    Article  MathSciNet  Google Scholar 

  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_2

    Chapter  Google Scholar 

  32. Wang, Z., Feng, J.: Multi-class learning from class proportions. Neurocomputing 119, 273–280 (2013)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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_29

    Chapter  Google Scholar 

  37. Wilcoxon, F.: Individual comparison by ranking methods. Biometr. Bull. 1(6), 80–83 (1945)

    Article  Google Scholar 

  38. Massey, F.J.: The Kolmogorov-Smirnov test for goodness of fit. J. Am. Stat. Assoc. 46(253), 68–78 (1951)

    Article  MATH  Google Scholar 

  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

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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”.

Author information

Authors and Affiliations

  1. Centro de Investigación Científica y de Educación Superior de Ensenada, Ensenada, Baja California, Mexico

    Gustavo Olague & Aaron Barrera

  2. Instituto Tecnológico de Ensenada, Ensenada, Baja California, Mexico

    Eddie Clemente

  3. Instituto Tecnológico de Tijuana, Tijuana, Baja California, Mexico

    Daniel E. Hernández

Authors
  1. Gustavo Olague
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  2. Eddie Clemente
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  3. Daniel E. Hernández
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  4. Aaron Barrera
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Corresponding author

Correspondence to Gustavo Olague .

Editor information

Editors and Affiliations

  1. Politecnico di Torino, Turin, Italy

    Giovanni Squillero

  2. Edinburgh Napier University, Edinburgh, United Kingdom

    Kevin Sim

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Olague, G., Clemente, E., Hernández, D.E., Barrera, A. (2017). Brain Programming and the Random Search in Object Categorization. In: Squillero, G., Sim, K. (eds) Applications of Evolutionary Computation. EvoApplications 2017. Lecture Notes in Computer Science(), vol 10199. Springer, Cham. https://doi.org/10.1007/978-3-319-55849-3_34

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  • DOI: https://doi.org/10.1007/978-3-319-55849-3_34

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-55848-6

  • Online ISBN: 978-3-319-55849-3

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