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Hierarchical image annotation using semantic hierarchies

Abstract : 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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Contributor : Céline Hudelot Connect in order to contact the contributor
Submitted on : Thursday, May 23, 2013 - 11:20:13 AM
Last modification on : Wednesday, April 8, 2020 - 3:29:22 PM


  • HAL Id : hal-00825214, version 1




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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