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

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.
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Contributor : Samuel Cruz-Lara Connect in order to contact the contributor
Submitted on : Wednesday, November 28, 2007 - 10:35:32 AM
Last modification on : Friday, February 4, 2022 - 3:34:19 AM
Long-term archiving on: : Monday, April 12, 2010 - 5:21:49 AM


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  • HAL Id : inria-00192463, version 1



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. ⟨inria-00192463⟩



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