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- W2082945265 abstract "With the development of Internet technology, a vast number of video data are available. Mining the hidden relationship among semantic concepts in video is important for effective content-based video retrieval and has gained great attention recently. Cloud computing, as a cost-effective solution, has become popular in mining video data for storing the distributed data and computations. In this paper, we have developed a novel method based on frequent pattern tree (FPTree) for mining association rules in video retrieval. The core of the method is to extend the structure of FPTree by temporal parameter in motion events. Firstly, we get semantic concepts based trajectory retrieval, Ncuts has been used to classify the sub-events by trajectory segmentation, and the sub-events in each event have been annotated. Secondly, the new modeling, called temporal frequent pattern tree (TFPTree) is used to store motion event semantic concepts. And we propose the TFP-Growth algorithm to mine temporal frequent patterns from TFPTree for finding the rules of the motion events. As video datasets grow large, cloud-based infrastructure has been used to support our computing. The experiment shows our method is both efficient and effective in improving the accuracy of semantic concept detection in video retrieval. Highlights? We extend the structure of FPTree by introducing temporal parameters. ? Five temporal relationships were used to describe temporal logic of sub-events. ?TFP-Growth has been proposed to mine temporal frequent patterns of motion events. ? Association rules among video events achieved better efficiency and effectiveness. ? The new method improved the accuracy of semantic concept detection." @default.
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- W2082945265 date "2013-01-01" @default.
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- W2082945265 title "The retrieval of motion event by associations of temporal frequent pattern growth" @default.
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