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Titlebook: Image and Video Retrieval; 4th International Co Wee-Kheng Leow,Michael S. Lew,Erwin M. Bakker Conference proceedings 2005 Springer-Verlag B

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书目名称Image and Video Retrieval
副标题4th International Co
编辑Wee-Kheng Leow,Michael S. Lew,Erwin M. Bakker
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Image and Video Retrieval; 4th International Co Wee-Kheng Leow,Michael S. Lew,Erwin M. Bakker Conference proceedings 2005 Springer-Verlag B
描述It was our great pleasure to host the 4th International Conference on Image and Video Retrieval (CIVR) at the National University of Singapore on 20–22 July 2005. CIVR aims to provide an international forum for the discussion of research challenges and exchange of ideas among researchers and practitioners in image/video retrieval technologies. It addresses innovative research in the broad ?eld of image and video retrieval. A unique feature of this conference is the high level of participation by researchers from both academia and industry. Another unique feature of CIVR this year was in its format – it o?ered both the traditional oral presentation sessions, as well as the short presentation cum poster sessions. The latter provided an informal alternative forum for animated discussions and exchanges of ideas among the participants. We are pleased to note that interest in CIVR has grown over the years. The number of submissions has steadily increased from 82 in 2002, to 119 in 2003, and 125 in 2004. This year, we received 128 submissions from the international communities:with81(63.3%)fromAsiaandAustralia,25(19.5%)fromEurope, and 22 (17.2%) from North America. After a rigorous review
出版日期Conference proceedings 2005
关键词Adobe Flash; DOM; MPEG; algorithms; architecture; content-based image retrieval; feature-based retrieval; i
版次1
doihttps://doi.org/10.1007/11526346
isbn_softcover978-3-540-27858-0
isbn_ebook978-3-540-31678-7Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer-Verlag Berlin Heidelberg 2005
The information of publication is updating

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Semantic Event Detection in Structured Video Using Hybrid HMM/SVM Markov Models (HMM) and guarantees high classification accuracy thanks to Support Vector Machines (SVM). The performance of the proposed method is compared with that of HMM based method, which shows the performance increase in both recall and precision of semantic event detection.
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