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HIGH ORDER STATISTICS FOR IMAGE CLASSIFICATION
Journal article

HIGH ORDER STATISTICS FOR IMAGE CLASSIFICATION

  • LABBI, ABDERRAHIM IBM Research, Zurich Research Lab., Saeumerstrasse 4, 8803 Rueschlikon, Switzerland
  • BOSCH, HOLGER Department of Computer Science, University of Geneva, Rue du General Dufour, 24, 1211 Geneve, Switzerland
  • PELLEGRINI, CHRISTIAN Department of Computer Science, University of Geneva, Rue du General Dufour, 24, 1211 Geneve, Switzerland
  • 2011-11-21
Published in:
  • International Journal of Neural Systems. - World Scientific Pub Co Pte Lt. - 2001, vol. 11, no. 04, p. 371-377
English This paper addresses the problem of image classification using local information which is aggregated to provide global representation of different image classes. Local information is adaptively extracted from an image database using Independent Component Analysis (ICA) which provides a set of localized, oriented, and band-pass filters selective to independent features of the images. Local representation using ICA techniques has been previously investigated by several researchers. However, very little work has been done on further use of these representations to provide more complex and global description of images. In this paper, we present an algorithm which uses the energy of a minimal set of ICA filters to provide class-specific signatures which are shown to be strongly discriminant. Computer simulations are carried on two image databases, one consisting of five classes -referred to as categories- (buildings, rooms, mountains, forests and beaches) and one consisting of a set of 30 objects from multiple views for viewpoint invariant object recognition. The classification performance of the algorithm using both Independent and Principal Component Analyses are reported and discussed.
Language
  • English
Open access status
closed
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Persistent URL
https://sonar.ch/global/documents/84384
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