Joint Hypergraph Learning for Tag-based Image Retrieval

Joint Hypergraph Learning for Tag-based Image Retrieval


As the image sharing websites like Flickr become more and more popular, extensive scholars concentrate on tag-based image retrieval (TBIR). It is one of the important ways to find images contributed by social users. In this research field, tag information and diverse visual features have been investigated. However, most existing methods use these visual features separately or sequentially. In this paper, we propose a global and local visual features fusion approach to learn the relevance of images by hypergraph approach. A hypergraph is constructed first by utilizing global, local visual features and tag information. Then, we propose a pseudo-relevance feedback mechanism to obtain the pseudo-positive images. Finally, with the hypergraph and pseudo relevance feedback, we adopt the hypergraph learning algorithm to calculate the relevance score of each image to the query. Experimental results demonstrate the effectiveness of the proposed approach.



  • System : Pentium Dual Core.
  • Hard Disk : 120 GB.
  • Monitor : 15’’ LED
  • Input Devices : Keyboard, Mouse
  • Ram : 1 GB


  • Operating system : Windows 7.
  • Coding Language : JAVA/J2EE
  • Tool : Netbeans 7.2.1
  • Database : MYSQL


Yaxiong Wang, Li Zhu, Xueming Qian, Member, IEEE, Junwei Han, “Joint Hypergraph Learning for Tag-based Image Retrieval”, IEEE Transactions on Image Processing, 2018.

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