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Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming

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Book cover Applications of Evolutionary Computation (EvoApplications 2012)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7248))

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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.

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Author information

Authors and Affiliations

  1. Proyecto EvoVision, Departamento de Ciencias de la Computación, División de Física Aplicada, Centro de Investigación Científica y de Estudios Superiores de Ensenada, Carretera Ensenada-Tijuana No. 3918, Zona Playitas, Ensenada, 22860, B.C., México

    Eddie Clemente, Gustavo Olague, León Dozal & Martín Mancilla

  2. Tecnológico de Estudios Superiores de Ecatepec, Avenida Tecnológico S/N, Esq. Av. Carlos Hank González, Valle de Anáhuac, Ecatepec de Morelos, México

    Eddie Clemente

Authors
  1. Eddie Clemente
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  2. Gustavo Olague
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  3. León Dozal
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  4. Martín Mancilla
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Editor information

Editors and Affiliations

  1. Department of Mathematics and Statistics, University of Strathclyde, 16 Richmond Street, G1 1XQ, Glasgow, UK

    Cecilia Di Chio

  2. Financial Mathematics and Computation Cluster Natural Computing Research and Applications Group Complex and Adaptive Systems Laboratory, University College, Dublin, Ireland

    Alexandros Agapitos

  3. Department of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  4. ETSI Informática, Universidad de Málaga, Campus de Teatinos, 29071, Málaga, Spain

    Carlos Cotta

  5. Centro Universitario de Merida, Universidad de Extremadura, Spain

    Francisco Fernández de Vega

  6. Dalle Molle Institute for Artificial Intelligence (IDSIA), Lugano, Switzerland

    Gianni A. Di Caro

  7. DFKI Bremen, Cyber-Physical Systems, Bremen, Germany

    Rolf Drechsler

  8. Knowledge Engineering Research Group, Aston University, B4 7ET, Aston Triangle, Birmingham, UK

    Anikó Ekárt

  9. Instituto Tecnológico de Informática, Edif. 8G Acceso B, Universidad Politécnica de Valencia, 46022, Valencia, Spain

    Anna I. Esparcia-Alcázar

  10. Next Generation Intelligent Networks Research Center (nexGIN RC), National University of Computer & Emerging Sciences (FAST-NU), Islamabad, Pakistan

    Muddassar Farooq

  11. Department of Computer Science,, University College London, Gower Street, WC1E 6BT, London, UK

    William B. Langdon

  12. Depto. Arquitectura y Tecnologa de Computadores, ETS Ingeiera Informtica, C/Daniel Saucedo Aranda, s/n, 18071, Granada, Spain

    Juan J. Merelo-Guervós

  13. Chair of Algorithm Engineering, Computational Intelligence Group, Dept. of Computer Science, Technische Universität Dortmund, Germany

    Mike Preuss

  14. HTWK Leipzig, Fakultät Elektrotechnik und Informationstechnik, Institut Mess–, Steuerungs– und Regelungstechnik, Postfach 30 11 66, D–04251, Leipzig, Germany

    Hendrik Richter

  15. INESC-ID Lisboa, Rua Alves Redol 9,, 1000-029, Lisboa, Portugal

    Sara Silva

  16. Centre for Informatics and Systems, University of Coimbra, Portugal

    Anabela Simões

  17. Dipartimento di Automatica e Informatica, Politecnico di Torino, Italy

    Giovanni Squillero

  18. Institute of High Performance Computing and Networking, ICAR-CNR, Naples, Italy

    Ernesto Tarantino

  19. Dipartimento di Tecnologie dell’Informazione, Universita’ degli Studi di Milano, Italy

    Andrea G. B. Tettamanzi

  20. IT University of Copenhagen, Rued Langaards Vej 7, 2300, Copenhagen, Denmark

    Julian Togelius

  21. Centre for Emergent Computing, Edinburgh Napier University, Edinburgh,, UK

    Neil Urquhart

  22. Department of Computer Engineering, Istanbul Technical University, 34490, Maslak, Turkey

    A. Şima Uyar

  23. Center For Computer Games Research, IT University Of Copenhagen, Rued Langgaards Vej 7, 2300, Copenhagen, Denmark

    Georgios N. Yannakakis

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© 2012 Springer-Verlag Berlin Heidelberg

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Clemente, E., Olague, G., Dozal, L., Mancilla, M. (2012). Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming. In: Di Chio, C., et al. Applications of Evolutionary Computation. EvoApplications 2012. Lecture Notes in Computer Science, vol 7248. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29178-4_32

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  • DOI: https://doi.org/10.1007/978-3-642-29178-4_32

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29177-7

  • Online ISBN: 978-3-642-29178-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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