k-Partite Graph Reinforcement and its Application in Multimedia Information Retrieval

• Main Authors: GAO, Yue, WANG, Meng, Ji, Rongrong, ZHA, Zheng-Jun, SHEN, Jialie
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2012
• Subjects: Multimedia; information retrieval; k-Partite; graph reinforcement; 3D object retrieval; Video retrieval; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/1497; https://ink.library.smu.edu.sg/context/sis_research/article/2496/viewcontent/auto_convert.pdf
• Institution: Singapore Management University

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