k-Partite graph reinforcement and its application in multimedia information retrieval

  • Authors:

  • Yue Gao;Meng Wang;Rongrong Ji;Zhengjun Zha;Jialie Shen

  • Affiliations:

  • Department of Control Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, PR China;School of Computer and Information, Hefei University of Technology, Hefei 230009, PR China;Department of Electronic Engineering, Columbia University, 10027 NY, United States;School of Computing, National University of Singapore, 117417 Singapore, Singapore;Singapore Management University, Singapore, Singapore

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

In many example-based information retrieval tasks, example query actually contains multiple sub-queries. For example, in 3D object retrieval, the query is an object described by multiple views. In content-based video retrieval, the query is a video clip that contains multiple frames. Without prior knowledge, the most intuitive approach is to treat the sub-queries equally without difference. In this paper, we propose a k-partite graph reinforcement approach to fuse these sub-queries based on the to-be-retrieved database. The approach first collects the top retrieved results. These results are regarded as pseudo-relevant samples and then a k-partite graph reinforcement is performed on these samples and the query. In the reinforcement process, the weights of the sub-queries are updated by an iterative process. We present experiments on 3D object retrieval and content-based video clip retrieval, and the results demonstrate that our method effectively boosts retrieval performance.