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  • Research Article
  • Open Access

Characterizing Image Sets Using Formal Concept Analysis

EURASIP Journal on Advances in Signal Processing20052005:628912

  • Received: 29 December 2003
  • Published:


This article presents a new method for supervised image classification. Given a finite number of image sets, each set corresponding to a place of an environment, we propose a localization strategy, which relies upon supervised classification. For each place, the corresponding landmark is actually a combination of features that have to be detected in the image set. Moreover, these features are extracted using a symbolic knowledge extraction theory, "formal concept analysis." This paper details the full landmark extraction process and its hierarchical organization. A real localization problem in a structured environment is processed as an illustration. This approach is compared with an optimized neural network-based classification, and validated with experimental results. Further research to build up hybrid classifier is outlined in the discussion.

Keywords and phrases

  • supervised classification
  • visual landmarks
  • Galois lattices
  • concept lattices
  • computer vision
  • localization

Authors’ Affiliations

National School of Aeronautics and Space (SUPAERO), 10 Edouard Belin Avenue, BP 54032, Toulouse Cedex, 31055, France
LAAS -CNRS, 7 Colonel Roche Avenue, Toulouse Cedex 4, 31077, France


© Zenou and Samuelides 2005