Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

9-2006

Abstract

The diverse and distributed nature of the information published on the World Wide Web has made it difficult to collate and track information related to specific topics. Whereas most existing work on web information fusion has focused on multiple document summarization, this paper presents a novel approach for discovering associations between images and text segments, which subsequently can be used to support cross-media web content summarization. Specifically, we employ a similarity-based multilingual retrieval model and adopt a vague transformation technique for measuring the information similarity between visual features and textual features. The experimental results on a terrorist domain document set suggest that combining visual and textual features provides a promising approach to image and text fusion.

Keywords

Textual feature, text segment, document summarization, Linear Mixture Model

Discipline

Databases and Information Systems | Graphics and Human Computer Interfaces

Research Areas

Data Science and Engineering

Publication

Knowledge Discovery in Databases: 17th European Conference, PKDD 2006, Berlin, Germany, September 18-22: Proceedings

Volume

4213

First Page

561

Last Page

568

ISBN

9783540453758

Identifier

10.1007/11871637_56

Publisher

Springer

City or Country

Cham

Additional URL

https://doi.org/10.1007/11871637_56

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