Improving haar cascade classifiers through the synthesis of new training examples

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

@InProceedings{Correia:2012:GECCOcomp,

• author = "Joao Correia and Penousal Machado and Juan Romero",
• title = "Improving haar cascade classifiers through the synthesis of new training examples",
• booktitle = "GECCO Companion '12: Proceedings of the fourteenth international conference on Genetic and evolutionary computation conference companion",
• year = "2012",
• editor = "Terry Soule and Anne Auger and Jason Moore and David Pelta and Christine Solnon and Mike Preuss and Alan Dorin and Yew-Soon Ong and Christian Blum and Dario Landa Silva and Frank Neumann and Tina Yu and Aniko Ekart and Will Browne and Tim Kovacs and Man-Leung Wong and Clara Pizzuti and Jon Rowe and Tobias Friedrich and Giovanni Squillero and Nicolas Bredeche and Stephen L. Smith and Alison Motsinger-Reif and Jose Lozano and Martin Pelikan and Silja Meyer-Nienberg and Christian Igel and Greg Hornby and Rene Doursat and Steve Gustafson and Gustavo Olague and Shin Yoo and John Clark and Gabriela Ochoa and Gisele Pappa and Fernando Lobo and Daniel Tauritz and Jurgen Branke and Kalyanmoy Deb",
• isbn13 = "978-1-4503-1178-6",
• keywords = "genetic algorithms, Genetic programming: Poster",
• pages = "1479--1480",
• month = "7-11 " # jul,
• organisation = "SIGEVO",
• address = "Philadelphia, Pennsylvania, USA",
• DOI = "doi:10.1145/2330784.2331001",
• publisher = "ACM",
• publisher_address = "New York, NY, USA",
• abstract = "A Genetic Programming approach for the improvement of the performance of classifier systems through the synthesis of new training instances is explored. Genetic Programming is used to exploit shortcomings of classifiers systems and generate misclassified instances. The proposed approach performs multiple parallel evolutionary runs to generate a large number of potentially misclassified samples. A supervisor module determines which of the generated images have been misclassified and which should be added to the training set. New classifiers are trained based on the original training set augmented by the selected evolved instances. The results attained while using face detection classifiers are presented and discussed. Overall they indicate that significant improvements are attained when using multiple evolutionary runs.",
• notes = "Also known as \cite{2331001} Distributed at GECCO-2012.

  ACM Order Number 910122.",

}

Genetic Programming entries for Joao Nuno Goncalves Costa Cavaleiro Correia Penousal Machado Juan Jesus Romero Cardalda

Citations