Multi-class particle swarm model selection for automatic image annotation

  • Authors:

  • Hugo Jair Escalante;Manuel Montes;L. Enrique Sucar

  • Affiliations:

  • National Institute of Astrophysics, Optics and Electronics, Department of Computational Sciences, Luis Enrique Erro #, 1 Tonantzintla, Puebla 72840, Mexico;National Institute of Astrophysics, Optics and Electronics, Department of Computational Sciences, Luis Enrique Erro #, 1 Tonantzintla, Puebla 72840, Mexico;National Institute of Astrophysics, Optics and Electronics, Department of Computational Sciences, Luis Enrique Erro #, 1 Tonantzintla, Puebla 72840, Mexico

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2012

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Abstract

This article describes the application of particle swarm model selection (PSMS) to the problem of automatic image annotation (AIA). PSMS can be considered a black-box tool for the selection of effective classifiers in binary classification problems. We face the AIA problem as one of multi-class classification, considering a one-vs-all (OVA) strategy. OVA makes a multi-class problem into a series of binary classification problems, each of which deals with whether a region belongs to a particular class or not. We use PSMS to select the models that compose the OVA classifier and propose a new technique for making multi-class decisions from the selected classifiers. This way, effective classifiers can be obtained in acceptable times; specific methods for preprocessing, feature selection and classification are selected for each class; and, most importantly, very good annotation performance can be obtained. We present experimental results in six data sets that give evidence of the validity of our approach; to the best of our knowledge the results reported herein are the best obtained so far in the data sets we consider. It is important to emphasize that despite the application domain we consider is AIA, nothing restricts us of applying the methods described in this article to any other multi-class classification problem. .