Advertisement

Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming

  • Eddie Clemente
  • Gustavo Olague
  • León Dozal
  • Martín Mancilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7248)

Abstract

Computational neuroscience is a discipline devoted to the study of brain function from an information processing standpoint. The ventral stream, also known as the “what” pathway, is widely accepted as the model for processing the visual information related to object identification. This paper proposes to evolve a mathematical description of the ventral stream where key features are identified in order to simplify the whole information processing. The idea is to create an artificial ventral stream by evolving the structure through an evolutionary computing approach. In previous research, the “what” pathway is described as being composed of two main stages: the interest region detection and feature description. For both stages a set of operations were identified with the aim of simplifying the total computational cost by avoiding a number of costly operations that are normally executed in the template matching and bag of feature approaches. Therefore, instead of applying a set of previously learned patches, product of an off-line training process, the idea is to enforce a functional approach. Experiments were carried out with a standard database and the results show that instead of 1200 operations, the new model needs about 200 operations.

Keywords

Evolutionary Artificial Ventral Stream Complex Designing System Heterogeneous and Hierarchical Genetic Programming 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bartlet, 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
  2. 2.
    Chih-Chung, C., Chih-Jen, L.: LIBSVM: a library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2(27), 1–27 (2011) Software available at, http://www.csie.ntu.edu.tw/~cjlin/libsvm Google Scholar
  3. 3.
    Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: IEEE Workshop on Generative-Model Based Vision, CVPR 2004 (2004)Google Scholar
  4. 4.
    Fukushima, K.: Necognitron: A Self-Organizing Neural Network Model for a Mechanism of Pattern Recognition Unaffected by Shift in Position. Biological Cybernetics 36, 193–202 (1980)MathSciNetCrossRefMATHGoogle Scholar
  5. 5.
    Holland, J.H.: Complex Adaptive Systems. A New Era in Computation 121(1), 17–30 (1993)MathSciNetGoogle Scholar
  6. 6.
    Hubel, D., Wiesel, T.: Receptive Fields of Single Neurones in the Cat Striate Cortex. J. Physiol. 148, 574–591 (1959)Google Scholar
  7. 7.
    Hubel, D.: Exploration of the Primary Visual Cortex. Nature 299, 515–524 (1982)CrossRefGoogle Scholar
  8. 8.
    LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based Learning applied to Document Recognition. Proceedings of the IEEE (1998)Google Scholar
  9. 9.
    Riesenhuber, M., Poggio, T.: Hierarchical Models of Object Recognition in Cortex. Nature Neuroscience 2(11), 1019–1025 (1999)CrossRefGoogle Scholar
  10. 10.
    Mutch, J., Lowe, D.: Object Class Recognition and Localization Using Sparse Features with Limited Receptive Fields. International Journal of Computer Vision, IJCV (2008)Google Scholar
  11. 11.
    Serre, T., Wolf, L., Bilechi, S., Riesenhuber, M., Poggio, T.: Robust Object Recognition with Cortex-Like Mechanisms. IEEE Transactions on Pattern Analysis and Machine Intelligence 29(3), 411–426 (2007)CrossRefGoogle Scholar
  12. 12.
    Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nature Neurosciencie 5(7), 682–687 (2002)Google Scholar
  13. 13.
    Ungerleider, L., Haxby, J.: “’What’ and ’where’ in the Human Brain”. Current Opinion in Neurobiology 4, 157–165 (1994)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Eddie Clemente
    • 1
    • 2
  • Gustavo Olague
    • 1
  • León Dozal
    • 1
  • Martín Mancilla
    • 1
  1. 1.Proyecto EvoVision, Departamento de Ciencias de la Computación, División de Física AplicadaCentro de Investigación Científica y de Estudios Superiores de EnsenadaEnsenadaMéxico
  2. 2.Tecnológico de Estudios Superiores de EcatepecEcatepec de MorelosMéxico

Personalised recommendations