Semantic mining technologies for multimedia databases
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XML Databases and the Semantic Web
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Semantic mining technologies for multimedia databases
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Dental Computing and Applications Andriani Daskalaki. Currently, he is an assistant professor with the Department of Computing in the Hong Kong Polytechnic University, is a visiting professor in the Xi'Dian University, and holds a visiting position at Birkbeck in Lon. His research interests include artificial intelligence, computer vision, data mining, geoinformatics, machine learning, multimedia, remote sensing, statistics, and visual surveillance. He co- chaired special sessions, invited sessions, workshops and conferences.
Semantic Mining Technologies for Multimedia Databases [Dacheng Tao, Dong Xu, Xuelong Li] on giuliettasprint.konfer.eu *FREE* shipping on qualifying offers. Download Citation on ResearchGate | Semantic mining technologies for multimedia databases | Multimedia searching and management have become popular.
He received the B. He also worked at Columbia University for one year as a postdoctoral research scientist.
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His research interests include computer vision, pattern recognition, statistical learning and multimedia content analysis. He is an associate editor of Neurocomputing Elsevier. He has over a hundred scientific publications with several Best Paper Awards and finalists.
He is also an associate editor editorial board member of ten other international journals and a guest co-editor of eight special issues. He has served as a chair of around twenty conferences and a program committee member for more than eighty conferences. He has been a reviewer for over a hundred journals and conferences, including eleven IEEE transactions.
Rating details. Book ratings by Goodreads. Most existing video analysis techniques focus on the low level visual features of video data. However, there is a "semantic gap" between the machine-readable features and the high level human concepts i.
In this paper, an interactive platform for semantic video mining and retrieval is proposed using relevance feedback RF , a popular technique in the area of content-based image retrieval CBIR. By tracking semantic objects in a video and then modeling spatio-temporal events based on object trajectories and object interactions, the proposed interactive learning algorithm in the platform is able to mine the spatio-temporal data extracted from the video. An iterative learning process is involved in the proposed platform, which is guided by the user's response to the retrieved results.
Although the proposed video retrieval platform is intended for general use and can be tailored to many applications, we focus on its application in traffic surveillance video database retrieval to demonstrate the design details.