Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4192

  author =       "Eddie Clemente and Gustavo Olague and Leon Dozal and 
                 Martin Mancilla",
  title =        "Object Recognition with an Optimized Ventral Stream
                 Model Using Genetic Programming",
  booktitle =    "Applications of Evolutionary Computing,
                 EvoApplications 2012: EvoCOMNET, EvoCOMPLEX, EvoFIN,
                 EvoGAMES, EvoHOT, EvoIASP, EvoNUM, EvoPAR, EvoRISK,
                 EvoSTIM, EvoSTOC",
  year =         "2011",
  month =        "11-13 " # apr,
  editor =       "Cecilia {Di Chio} and Alexandros Agapitos and 
                 Stefano Cagnoni and Carlos Cotta and F. {Fernandez de Vega} and 
                 Gianni A. {Di Caro} and Rolf Drechsler and 
                 Aniko Ekart and Anna I Esparcia-Alcazar and Muddassar Farooq and 
                 William B. Langdon and Juan J. Merelo and 
                 Mike Preuss and Hendrik Richter and Sara Silva and 
                 Anabela Simoes and Giovanni Squillero and Ernesto Tarantino and 
                 Andrea G. B. Tettamanzi and Julian Togelius and 
                 Neil Urquhart and A. Sima Uyar and Georgios N. Yannakakis",
  series =       "LNCS",
  volume =       "7248",
  publisher =    "Springer Verlag",
  address =      "Malaga, Spain",
  publisher_address = "Berlin",
  pages =        "315--325",
  organisation = "EvoStar",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-3-642-29177-7",
  DOI =          "doi:10.1007/978-3-642-29178-4_32",
  size =         "11 pages",
  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
                 learnt 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.",
  notes =        "EvoIASP Part of \cite{DiChio:2012:EvoApps}
                 EvoApplications2012 held in conjunction with
                 EuroGP2012, EvoCOP2012, EvoBio'2012 and EvoMusArt2012",
  affiliation =  "Proyecto EvoVision, Departamento de Ciencias de la
                 Computacion, Division de Fisica Aplicada, Centro de
                 Investigacion Cientifica y de Estudios Superiores de
                 Ensenada, Carretera Ensenada-Tijuana No. 3918, Zona
                 Playitas, Ensenada, 22860 B.C., Mexico",

Genetic Programming entries for Eddie Helbert Clemente Torres Gustavo Olague Leon Dozal Martin Mancilla