Abstract
This chapter describes a new approach to synthesize an artificial visual cortex based on what we call brain 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 only a few. These features are obtained by a complex arrangement of highly interconnected and numerous cortical visual areas. This chapter 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 brain 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 discovered through the application of multiple runs of genetic programming that works concurrently using the hierarchical structure. Experimental results provide evidence that high recognition rates could be achieved for a well-known multiclass object recognition problem.
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References
Ayala, F.J.: Teleological Explanations in Evolutionary Biology. Philosophy of Science 37(1), 1–15 (1970)
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)
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 http://www.csie.ntu.edu.tw/~cjlin/libsvm
Clemente, E., Olague, G., Dozal, L., Mancilla, M.: Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming. In: Di Chio, C., et al. (eds.) EvoApplications 2012. LNCS, vol. 7248, pp. 315–325. Springer, Heidelberg (2012)
Creem, S.H., Proffitt, D.R.: Defining the cortical visual systems: “what”, “where”, and “how”. Acta Psychologica 107, 43–68 (2001)
Desimone, R., Duncan, J.: Neural mechanisms of selective visual attention. Annu. Rev. Neurosci. 18, 193–222 (1995)
Fei-Fei, L., Fergus, R., Perona, P.: Learning generative visual models from few training examples: An incremental Bayesian approach tested on 101 object categories. Computer Vision and Image Understanding 106(1), 59–70 (2007)
Fukushima, K.: Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics 36(4), 193–202 (1980)
Holland, J.H.: Complex Adaptive Systems. Daedalus 121(1), 17–30 (1992)
Hoquet, T.: Darwin teleologist? Design in the orchids. Comptes Rendus Biologies 333(2), 119–128 (2010)
Hubel, D.H.: Exploration of the primary visual cortex, 1955-78. Nature, 515–524 (October 1982)
Hubel, D.H., Wiesel, T.N.: Receptive fields of single neurones in the cat’s striate cortex. J. Physiol. 148(3), 574–591 (1953)
Itti, L., Koch, C.: Computational modelling of visual attention. Nature Review Neuroscience 2(3), 194–203 (2001)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4(4), 219–227 (1985)
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278–2324 (1998)
Lennox, J.G.: Darwin was a teleologist. Biology and Philosophy 8(4), 409–421 (1993)
Mel, B.W.: Seemore: Combining Color, Shape, and Texture Histogramming in a Neurally Inspired Approach to Visual Object Recognition. Neural Computation 9(4), 777–804 (1997)
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)
Milner, A.D., Goodale, M.A.: The Visual Brain in Action, 2nd edn. Oxford University Press, Oxford (2006)
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)
Olague, G.: Evolutionary Computer Vision – The First Footprints (to appear)
Oram, M.W., Perrett, D.I.: Modeling visual recognition from neurobiological constraints. Neural Networks 7(6), 945–972 (1994)
Rensink, R.A.: The Dynamic Representation of Scenes. Visual Cognition 7(1-3), 17–42 (2000)
Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience 2, 1019–1025 (1999)
Schneider, G.E.: Contrasting Visuomotor Functions of Tectum and Cortex in the Golden Hamster. Psychologische Forschung 31(1), 52–62 (1967)
Schneider, G.E.: Two Visual Systems. Science 163(3870), 895–902 (1969)
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)
Short, T.L.: Darwin’s concept of final cause: neither new nor trivial. Biology and Philosophy 17, 323–340 (2002)
Treisman, A.M., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)
Ullman, S., Vidal-Naquet, M., Sali, E.: Visual features of intermediate complexity and their use in classification. Nature Neuroscience 5(7), 682–687 (2002)
Ungerleider, L.G., Haxby, J.V.: “What” and “where” in the human brain. Current Opinion in Neurobiology 4(2), 157–165 (1994)
Mishkin, M.M., Ungerleider, L.G., Macko, K.A.: Object vision and spatial vision: two cortical pathways. Trends in Neurosciences 6, 414–417 (1983)
Walther, D., Koch, C.: Modeling attention to salient proto-objects. Neural Networks 19(9), 1395–1407 (2006)
Wolfe, J.M., Horowitz, T.S.: What attributes guide the deployment of visual attention and how do they do it? Nat. Rev. Neurosci. 5(6), 495–501 (2004)
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Olague, G., Clemente, E., Dozal, L., Hernández, D.E. (2014). Evolving an Artificial Visual Cortex for Object Recognition with Brain Programming. In: Schuetze, O., et al. EVOLVE - A Bridge between Probability, Set Oriented Numerics, and Evolutionary Computation III. Studies in Computational Intelligence, vol 500. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01460-9_5
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