Multi-class classification of objects in images using principal component analysis and genetic programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Manasses Ribeiro and Heitor Silverio Lopes",
  booktitle =    "2015 Latin America Congress on Computational
                 Intelligence (LA-CCI)",
  title =        "Multi-class classification of objects in images using
                 principal component analysis and genetic programming",
  year =         "2015",
  abstract =     "This work presents a methodology for using Principal
                 Component Analysis (PCA) and Genetic Programming (GP)
                 for the classification of multi-class objects found in
                 digital images. The image classification process is
                 performed by using features extracted from images,
                 through feature extraction algorithms, reduced by PCA
                 and labelled by similarity comparing with other
                 previously classified objects. GP uses two sets of
                 elements: terminals, composed by the features extracted
                 by PCA; and non-terminals, composed by algebraic
                 operations. The fitness function was defined by the
                 product of sensibility and specificity, two performance
                 measures. A penalty term is also used to decrease the
                 number of nodes of the tree, while minimally affecting
                 the quality of solutions. The proposed approach was
                 applied to set of 2739 digital images divided into
                 objects representing airplanes, motorbikes, background
                 from google, faces and watch classes, provided by the
                 Caltech101 image database. The proposed approach was
                 compared with SVM, Naive Bayes and C4.5. Results
                 suggest that the approach PCA+GP is able to evolve
                 solutions for the problem as a simple classification
                 rule with true positive rate above 70percent.
                 Additionally, we observe that PCA+PG obtained results
                 slightly better than SVM and C4.5, besides these
                 methods give a result that is not comprehensible by
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/LA-CCI.2015.7435982",
  month =        oct,
  notes =        "Also known as \cite{7435982}",

Genetic Programming entries for Manasses Ribeiro Heitor Silverio Lopes