Object Recognition with an Optimized Ventral Stream Model Using Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{Clemente:evoapps12,

• 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

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