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Video segmentation combining similarity analysis and classification
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Source International Multimedia Conference archive
Proceedings of the 12th annual ACM international conference on Multimedia table of contents
New York, NY, USA
POSTER SESSION: Technical poster session 1: multimedia analysis, processing, and retrieval table of contents
Pages: 252 - 255  
Year of Publication: 2004
ISBN:1-58113-893-8
Author
Matthew Cooper  FX Palo Alto Laboratory, Palo Alto, CA
Sponsors
SIGMULTIMEDIA: ACM Special Interest Group on Multimedia
ACM: Association for Computing Machinery
Publisher
ACM  New York, NY, USA
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ABSTRACT

In this paper, we compare several recent approaches to video segmentation using pairwise similarity. We first review and contrast the approaches within the common framework of similarity analysis and kernel correlation. We then combine these approaches with non-parametric supervised classification for shot boundary detection. Finally, we discuss comparative experimental results using the 2002 TRECVID shot boundary detection test collection.


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
A. Smeaton and P. Over. The TREC 2002 Video Track Report. Proc. TREC Video Track, 2002.
 
2
B. Gunsel, M. Ferman, and A. M. Tekalp, Temporal video segmentation using unsupervised clustering and semantic object tracking, J. Electronic Imaging 7(3):592--604, July 1998.
 
3
M. Cooper and J. Foote. Scene Boundary Detection Via Video Self-Similarity Analysis. Proc. IEEE Intl. Conf. on Image Processing, 2001.
 
4
J. Boreczky and L. Rowe. Comparison of video shot boundary detection techniques. Proc. SPIE Storage and Retrieval for Image and Video Databases, 1996.
 
5
A. Witkin. Scale-space Filtering: A New Approach to Multi-scale Description. Proc. IEEE ICASSP, 1984.
6
 
7
D. Pye, N. Hollinghurst, T. Mills, and K. Wood. Audio-visual Segmentation for Content-Based Retrieval. Proc. Intl. Conf on Spoken Language Processing, 1998.
 
8
M. Pickering, D. Heesch, et al.. Video Retrieval using Global Features in Keyframes. Proc. TREC Video Track, 2002.
 
9
Y. Qi, A. Hauptman, and T. Liu. Supervised Classification for Video Shot Segmentation. Proc. IEEE Intl. Conf. on Multimedia & Expo, 2003.
 
10
T. Liu, A. Moore, and A. Gray. Efficient Exact k-NN and Nonparametric Classification in High Dimensions. Proc. Neural Information Processing Systems, 2003.