Generating image descriptions using dependency relational patterns

  • Authors:
  • Ahmet Aker;Robert Gaizauskas

  • Affiliations:
  • University of Sheffield;University of Sheffield

  • Venue:
  • ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
  • Year:
  • 2010

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Abstract

This paper presents a novel approach to automatic captioning of geo-tagged images by summarizing multiple web-documents that contain information related to an image's location. The summarizer is biased by dependency pattern models towards sentences which contain features typically provided for different scene types such as those of churches, bridges, etc. Our results show that summaries biased by dependency pattern models lead to significantly higher ROUGE scores than both n-gram language models reported in previous work and also Wikipedia baseline summaries. Summaries generated using dependency patterns also lead to more readable summaries than those generated without dependency patterns.