Open Access

Characterizing Image Sets Using Formal Concept Analysis

EURASIP Journal on Advances in Signal Processing20052005:628912

Received: 29 December 2003

Published: 15 August 2005


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)


© Zenou and Samuelides 2005