Hierarchical image annotation using semantic hierarchies - Archive ouverte HAL
Communication Dans Un Congrès Année : 2012

Hierarchical image annotation using semantic hierarchies

Résumé

Semantic hierarchies have been introduced recently to improve image annotation. They was used as a framework for hierarchical image classification, and thus to improve classifiers accuracy and reduce the complexity of managing large scale data. In this paper, we investigate the contribution of semantic hierarchies for hierarchical image classification. We propose first a new method based on the hierarchy structure to train efficiently hierarchical classifiers. Our method, named One-Versus-Opposite-Nodes, allows decomposing the problem in several independent tasks and therefore scales well with large database. We also propose two methods for computing a hierarchical decision function that serves to annotate new image samples. The former is performed by a top-down classifiers voting, while the second is based on a bottom-up score fusion. The experiments on Pascal VOC'2010 dataset showed that our methods improve well the image annotation results.
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Dates et versions

hal-00825214 , version 1 (23-05-2013)

Identifiants

  • HAL Id : hal-00825214 , version 1

Citer

Hichem Bannour, Céline Hudelot. Hierarchical image annotation using semantic hierarchies. CIKM '12 Proceedings of the 21st ACM international conference on Information and knowledge management, 2012, Sheraton, United States. pp.2431-2434. ⟨hal-00825214⟩

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