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Discovering Image-Text Associations for Cross-Media Web Information Fusion

  • Tao Jiang
  • Ah-Hwee Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4213)

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 Word Space Linear Mixture Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Tao Jiang
    • 1
  • Ah-Hwee Tan
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

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