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Characterizing Image Sets Using Formal Concept Analysis

Abstract

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.

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Correspondence to Emmanuel Zenou.

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Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License ( https://creativecommons.org/licenses/by/2.0 ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Zenou, E., Samuelides, M. Characterizing Image Sets Using Formal Concept Analysis. EURASIP J. Adv. Signal Process. 2005, 628912 (2005). https://doi.org/10.1155/ASP.2005.1931

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  • DOI: https://doi.org/10.1155/ASP.2005.1931

Keywords and phrases

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