Content-based video similarity model

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

Content-based video similarity model

Full text

Pdf (330 KB)

Source

International Multimedia Conference archive
Proceedings of the eighth ACM international conference on Multimedia table of contents

Marina del Rey, California, United States

Pages: 465 - 467

Year of Publication: 2000

ISBN:1-58113-198-4

Authors

Yi Wu	Institute of Artificial Intelligence, Zhejiang University, Microsoft Visual Perception Laboratory of Zhejiang University, Hangzhou, 310027, P.R.China
Yueting Zhuang	Institute of Artificial Intelligence, Zhejiang University, Microsoft Visual Perception Laboratory of Zhejiang University, Hangzhou, 310027, P.R.China
Yunhe Pan	Institute of Artificial Intelligence, Zhejiang University, Microsoft Visual Perception Laboratory of Zhejiang University, Hangzhou, 310027, P.R.China

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction
SIGCOMM: ACM Special Interest Group on Data Communication
SIGIR: ACM Special Interest Group on Information Retrieval
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
SIGOPS: ACM Special Interest Group on Operating Systems
SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques
SIGMIS: ACM Special Interest Group on Management Information Systems

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 9, Downloads (12 Months): 42, Citation Count: 7

Additional Information:

abstract references cited by index terms collaborative colleagues

Tools and Actions:	Request Permissions Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/354384.376380 What is a DOI?

ABSTRACT

The most commonly used method for content-based video retrieval is query by example. But the definition of video similarity brings great obstacle to further research. This paper puts forward a new approach to solve the difficulty. Firstly, it advances centroid feature vector of shot in order to reduce the storage of video database. Secondly, considering all the factors existing in human vision perception, it introduces a new comparison algorithm based on multi-granularity of video structure, which has great flexibility. Thirdly, after getting the similar video set, we take a brand-new method of feedback to adjust weight based on video similarity model. In this way, retrieval result can be optimized greatly.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	Madirakshi Das , Shih-Ping Liou, A New Hybrid Approach to Video Organization for Content-Based Indexing, Proceedings of the IEEE International Conference on Multimedia Computing and Systems, p.372, June 28-July 01, 1998 [doi>10.1109/ICMCS.1998.10000]
	2	Nevenka Dimitrova and Mohamed Abdel-Mottaled, "content-based Video retrieval by example video clip." In:SPIE 3022,1998
	3	Rainer Lienhart, Wolfgang Effelsberg, Ramesh Jain, "VisualGREP: A systematic methord to compare and retrieval video sequences". In: SPIE Vol.3312, pp.271-282,1997.
	4	H.Tamura, S.Mori,and T.Yamawaki, "Texture features corresponding to visual perception," IEEE Trans. on Sys, Man, and Cyb, vol.SMC-8,no.6,1978.

CITED BY 7

	JungHwan Oh , Maruthi Thenneru , Ning Jiang, Hierarchical video indexing based on changes of camera and object motions, Proceedings of the 2003 ACM symposium on Applied computing, March 09-12, 2003, Melbourne, Florida
	Yuxin Peng , Chong-Wah Ngo, Clip-based similarity measure for hierarchical video retrieval, Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval, October 15-16, 2004, New York, NY, USA
	Justin Zobel , Timothy C. Hoad, Detection of video sequences using compact signatures, ACM Transactions on Information Systems (TOIS), v.24 n.1, p.1-50, January 2006
	Timothy C. Hoad , Justin Zobel, Video similarity detection for digital rights management, Proceedings of the twenty-sixth Australasian conference on Computer science: research and practice in information technology, p.237-245, February 01, 2003, Adelaide, Australia
	Yuxin Peng , Chong-Wah Ngo , Jianguo Xiao, OM-based video shot retrieval by one-to-one matching, Multimedia Tools and Applications, v.34 n.2, p.249-266, August 2007
	Ja-Hwung Su , Yu-Ting Huang , Vincent S. Tseng, Efficient content-based video retrieval by mining temporal patterns, Proceedings of the 9th International Workshop on Multimedia Data Mining: held in conjunction with the ACM SIGKDD 2008, p.36-42, August 24-24, 2008, Las Vegas, Nevada
	Zi Huang , Heng Tao Shen , Jie Shao , Xiaofang Zhou , Bin Cui, Bounded coordinate system indexing for real-time video clip search, ACM Transactions on Information Systems (TOIS), v.27 n.3, p.1-33, May 2009