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

Yue Gao, Meng Wang, Rongrong Ji, Zhengjun Zha, Jialie Shen

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)224-239
Number of pages16
JournalInformation Sciences
Publication statusPublished - 1 Jul 2012
Externally publishedYes


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