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2019 | OriginalPaper | Buchkapitel

Uncovering Hidden Links Between Images Through Their Textual Context

verfasst von : Hatem Aouadi, Mouna Torjmen Khemakhem, Maher Ben Jemaa

Erschienen in: Enterprise Information Systems

Verlag: Springer International Publishing

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Abstract

Using hyperlinks to enhance page ranking has been widely studied in the literature. The main motivation is that an hyperlink underlines a page relevance. However, several hyperlinks in the web are used for navigation or marketing purposes. In addition, hyperlinks are created manually, so it is impossible to semantically link all similar pages. In our work, we propose to uncover hidden semantic links and create them automatically between all the collection’s images. For this aim, we propose first to format textual context of images into topic distributions via LDA technique, and then compute semantic similarities to create links. Experiments carried out in the Wikipedia Retrieval Task of ImageClef 2011 showed that the whole textual context of images is useful for uncovering hidden links and consequently enhancing the retrieval accuracy.

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Vorheriges Kapitel Recommending Semantic Concepts for Improving the Process of Semantic Modeling
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Metadaten
Titel
Uncovering Hidden Links Between Images Through Their Textual Context
verfasst von
Hatem Aouadi
Mouna Torjmen Khemakhem
Maher Ben Jemaa
Copyright-Jahr
2019
Buch
Enterprise Information Systems

Print ISBN: 978-3-030-26168-9

Electronic ISBN: 978-3-030-26169-6

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-3-030-26169-6_18