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An integrated scheme for object-based video abstraction
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Source International Multimedia Conference archive
Proceedings of the eighth ACM international conference on Multimedia table of contents
Marina del Rey, California, United States
Pages: 303 - 311  
Year of Publication: 2000
ISBN:1-58113-198-4
Authors
Changick Kim  Information processing Laboratory, Dept. of Electrical Engineering, Box#352500, University of Washington, Seattle, WA
Jenq-Neng Hwang  Information processing Laboratory, Dept. of Electrical Engineering, Box#352500, University of Washington, Seattle, WA
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
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Downloads (6 Weeks): 10,   Downloads (12 Months): 64,   Citation Count: 12
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ABSTRACT

In this paper, we present a novel scheme for object-based key-frame extraction facilitated by an efficient video object segmentation system. Key-frames are the subset of still images which best represent the content of a video sequence in an abstracted manner. Thus, key-frame based video abstraction transforms an entire video clip to a small number of representative images. The challenge is that the extraction of key-frames needs to be automated and context dependent so that they maintain the important contents of the video while remove all redundancy. Among various semantic primitives of video, objects of interest along with their actions and generated events can play an important role in some applications such as object-based video surveillance system. Furthermore, on-line processing combined with fast and robust video object segmentation is crucial for real-time applications to report unwanted action or event as soon as it happens. Experimental results on the proposed scheme for object-based video abstraction are presented.


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
T. Sikora, "The MPEG-4 video standard verification model," IEEE Trans. Circuits Syst. Video Technology, vol. 7, pp.19-31, Feb. 1997.
 
2
William E. Grim.son, From Images to Surfaces, The MIT press, pp3-5, 1981.
 
3
 
4
H.J.Zhang et al. "An Integrated system for Content-based Video Retrieval and Browing", Pattern recognition, vol. 30, No.4, pp.643-658, 1997.
 
5
 
6
 
7
A.M.Ferman et al., "Object-Based Indexing of MPEG-4 Compressed Video", SPIE-3024, pp. 953-963, Feb. 1997, San Jose, CA.
 
8
 
9
 
10
C. Gu and M-C Lee, "Semantic Segmentation and Tracking of Semantic Video objects," IEEE Trans. Orcuits Syst. Video Technology, vol. 8, pp.572-584, Sep. 1998.
 
11
L. Shapiro, "Computer Vision," Prentice Hall, to be published.
 
12
htto://students.washington.edu/cikim/cidil/mos/mos3.html
 
14
M.Wollborn and R. Mech, "Refined procedure for objective evaluation of video generation algorithms," Doc. ISO/IEC JTCl/SC29/WG11 M3448, March, 1998.
 
15
M. Hu, "Visual pattern recognition by moment invariants", IRE Trans. Information Theory, IT-8(2), pp. 179-182, Feb. 1962.
 
16
Ju Guo et al,, "Fast and accurate moving object extraction technique for MPEG-4 object-based video coding," SPIE, vol.3653, pp.1210-1221, January, 1999.
 
17
Changick Kim and Jenq-Neng Hwang, "Fast and Robust Moving Object Segmentation in Video Sequences," IEEE international conference on Image Processing (ICIP'99), Kobe, Japan, Oct. 1999.

CITED BY  12

Collaborative Colleagues:
Changick Kim: colleagues
Jenq-Neng Hwang: colleagues