Context-based image retrieval: A case study in background image access for Multimedia presentations

Sheng-Hao Hung 1 Pai-Hsun Chen 2 Jen-Shin Hong 1 Samuel Cruz-Lara 3
1 National ChiNan University
2 Nan-Kai Institute of Technology
3 TALARIS - Natural Language Processing: representation, inference and semantics
Inria Nancy - Grand Est, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Conventional approaches of image indexing and retrieval from digital libraries include content-based, metadata-based, and keyword-based approaches. This paper addresses a different way of image retrieval motivated by real-life applications for an intelligent system that can automatically select appropriate background images from textual passages. We explored techniques for developing automatic image-retrieval systems based on essential contextual information of a textual passage. We propose a framework that applies semantic role labeling techniques and a commonsense knowledge base, ConceptNet. The primitive results indicate that the proposed methodology has a potential on applications with textual passages that describe things and events that are regularly seen in every day life. However, for fantasy tales that describe truly fictitious things and events, the use of ConceptNet does not allow to obtain accurate results.
Keywords : context-based image retrieval knowledge base semantic role labeling ConceptNet Multimedia
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Communication dans un congrès
IADIS International Conference WWW/Internet 2007, Oct 2007, Vila Real, Portugal. 2007
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Dernière modification le : jeudi 11 janvier 2018 - 06:21:35
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Sheng-Hao Hung, Pai-Hsun Chen, Jen-Shin Hong, Samuel Cruz-Lara. Context-based image retrieval: A case study in background image access for Multimedia presentations. IADIS International Conference WWW/Internet 2007, Oct 2007, Vila Real, Portugal. 2007. 〈inria-00192463〉

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