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A Comparative Study of Local Descriptors for Object Category Recognition: SIFT vs HMAX

  • Plinio Moreno
  • Manuel J. Marín-Jiménez
  • Alexandre Bernardino
  • José Santos-Victor
  • Nicolás Pérez de la Blanca
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4477)

Abstract

In this paper we evaluate the performance of the two most successful state-of-the-art descriptors, applied to the task of visual object detection and localization in images. In the first experiment we use these descriptors, combined with binary classifiers, to test the presence/absence of object in a target image. In the second experiment, we try to locate faces in images, by using a structural model. The results show that HMAX performs slightly better than SIFT in these tasks.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Plinio Moreno
    • 1
  • Manuel J. Marín-Jiménez
    • 2
  • Alexandre Bernardino
    • 1
  • José Santos-Victor
    • 1
  • Nicolás Pérez de la Blanca
    • 2
  1. 1.Instituto Superior Técnico & Instituto de Sistemas e Robótica, 1049-001 LisboaPortugal
  2. 2.Dpt. Computer Science and Artificial Intelligence, University of Granada, ETSI Informática y Telecomunicación, Granada, 18071Spain

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