Tag-based social image search with visual-text joint hypergraph learning

  • Authors:

  • Yue Gao;Meng Wang;Huanbo Luan;Jialie Shen;Shuicheng Yan;Dacheng Tao

  • Affiliations:

  • Tsinghua University, Beijing, Singapore;National University of Singapore, Singapore, Singapore;National University of Singapore, Singapore, Singapore;Singapore Management University, Singapore, Singapore;National University of Singapore, Singapore, Singapore;University of Technology, Sydney, Sydney, Australia

  • Venue:
  • MM '11 Proceedings of the 19th ACM international conference on Multimedia
  • Year:
  • 2011

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Abstract

Tag-based social image search has attracted great interest and how to order the search results based on relevance level is a research problem. Visual content of images and tags have both been investigated. However, existing methods usually employ tags and visual content separately or sequentially to learn the image relevance. This paper proposes a tag-based image search with visual-text joint hypergraph learning. We simultaneously investigate the bag-of-words and bag-of-visual-words representations of images and accomplish the relevance estimation with a hypergraph learning approach. Each textual or visual word generates a hyperedge in the constructed hypergraph. We conduct experiments with a real-world data set and experimental results demonstrate the effectiveness of our approach.