ACM Home Page
Please provide us with feedback. Feedback
Bounded coordinate system indexing for real-time video clip search
Full text PdfPdf (1.93 MB)
Source
ACM Transactions on Information Systems (TOIS) archive
Volume 27 ,  Issue 3  (May 2009) table of contents
Article No. 17  
Year of Publication: 2009
ISSN:1046-8188
Authors
Zi Huang  The University of Queensland, Australia
Heng Tao Shen  The University of Queensland, Australia
Jie Shao  The University of Queensland, Australia
Xiaofang Zhou  The University of Queensland, Australia
Bin Cui  Peking University, China
Publisher
ACM  New York, NY, USA
Bibliometrics
Downloads (6 Weeks): 43,   Downloads (12 Months): 165,   Citation Count: 0
Additional Information:

abstract   references   index terms   collaborative colleagues  

Tools and Actions: Request Permissions Request Permissions    Review this Article  
DOI Bookmark: Use this link to bookmark this Article: http://doi.acm.org/10.1145/1508850.1508855
What is a DOI?

ABSTRACT

Recently, video clips have become very popular online. The massive influx of video clips has created an urgent need for video search engines to facilitate retrieving relevant clips. Different from traditional long videos, a video clip is a short video often expressing a moment of significance. Due to the high complexity of video data, efficient video clip search from large databases turns out to be very challenging. We propose a novel video clip representation model called the Bounded Coordinate System (BCS), which is the first single representative capturing the dominating content and content—changing trends of a video clip. It summarizes a video clip by a coordinate system, where each of its coordinate axes is identified by principal component analysis (PCA) and bounded by the range of data projections along the axis. The similarity measure of BCS considers the operations of translation, rotation, and scaling for coordinate system matching. Particularly, rotation and scaling reflect the difference of content tendencies. Compared with the quadratic time complexity of existing methods, the time complexity of measuring BCS similarity is linear. The compact video representation together with its linear similarity measure makes real-time search from video clip collections feasible. To further improve the retrieval efficiency for large video databases, a two-dimensional transformation method called Bidistance Transformation (BDT) is introduced to utilize a pair of optimal reference points with respect to bidirectional axes in BCS. Our extensive performance study on a large database of more than 30,000 video clips demonstrates that BCS achieves very high search accuracy according to human judgment. This indicates that content tendencies are important in determining the meanings of video clips and confirms that BCS can capture the inherent moment of video clip to some extent that better resembles human perception. In addition, BDT outperforms existing indexing methods greatly. Integration of the BCS model and BDT indexing can achieve real-time search from large video clip databases.


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
2
 
3
 
4
Bertini, M., Bimbo, A. D., and Nunziati, W. 2006. Video clip matching using mpeg-7 descriptors and edit distance. In Proceedings of the CIVR. 133--142.
5
 
6
 
7
Chang, H. S., Sull, S., and Lee, S. U. 1999. Efficient video indexing scheme for content-based retrieval. IEEE Trans. Circ. Syst. Video Tech. 9, 8, 1269--1279.
 
8
Chen, L. and Chua, T.-S. 2001. A match and tiling approach to content-based video retrieval. In Proceedings of ICME. 417--420.
 
9
Chen, L., Özsu, M. T., and Oria, V. 2004. Mindex: An efficient index structure for salient-object-based queries in video databases. Multimed. Syst. 10, 1, 56--71.
10
 
11
Cheung, S.-C. S. and Zakhor, A. 2003. Efficient video similarity measurement with video signature. IEEE Trans. Circ. Syst. Video Tech. 13, 1, 59--74.
 
12
Cheung, S.-C. S. and Zakhor, A. 2005. Fast similarity search and clustering of video sequences on the world-wide-Web. IEEE Trans. Multimed. 7, 3, 524--537.
 
13
14
15
16
 
17
Ferman, A. M. and Tekalp, A. M. 2003. Two-stage hierarchical video summary extraction to match low-level user browsing preferences. IEEE Trans. Multimed. 5, 2, 244--256.
 
18
Franco, A., Lumini, A., and Maio, D. 2007. MKL-tree: An index structure for high-dimensional vector spaces. Multimed. Syst. 12, 6, 533--550.
 
19
 
20
Gibbon, D. C. 2005. Introduction to video search engines. In Proceedings of WWW.Tutorial.
 
21
 
22
Hampapur, A., Hyun, K.-H., and Bolle, R. M. 2002. Comparison of sequence matching techniques for video copy detection. In Proceedings of SPIE: Storage and Retrieval for Image and Video Databases. 194--201.
 
23
Ho, Y.-H., Lin, C.-W., Chen, J.-F., and Liao, H.-Y. M. 2006. Fast coarse-to-fine video retrieval using shot-level spatio-temporal statistics. IEEE Trans. Circ. Syst. Video Tech. 16, 5, 642--648.
24
 
25
 
26
Iyengar, G. and Lippman, A. 2000. Distributional clustering for efficient content-based retrieval of images and video. In Proceedings of ICIP. 81--84.
27
 
28
Jolliffe, I. T. 2002. principal component Analysis, 2nd ed. Springer-Verlag, Berlin, Germany.
 
29
Kashino, K., Kurozumi, T., and Murase, H. 2003. A quick search method for audio and video signals based on histogram pruning. IEEE Trans. Multimed. 5, 3, 348--357.
 
30
 
31
Kim, C. and Vasudev, B. 2005. Spatiotemporal sequence matching for efficient video copy detection. IEEE Trans. Circ. Syst. Video Tech. 15, 1, 127--132.
32
33
 
34
 
35
Lienhart, R. 1999. Comparison of automatic shot boundary detection algorithms. In Proceedings of SPIE: Storage and Retrieval for Image and Video Databases. 209--301.
36
 
37
Mohan, R. 1998. Video sequence matching. In Proceedings of the ICASSP. 3697--3700.
 
38
Naphade, M. R., Yeung, M. M., and Yeo, B.-L. 2000. A novel scheme for fast and efficient video sequence matching using compact signatures. In Proceedings of SPIE: Storage and Retrieval for Image and Video Databases. 564--572.
 
39
Peng, Y. and Ngo, C.-W. 2006. Clip-based similarity measure for query-dependent clip retrieval and video summarization. IEEE Trans. Circ. Syst. Video Tech. 16, 5, 612--627.
 
40
 
41
42
 
43
44
45
 
46
47
 
48
 
49
50
51
52
 
53
Zhu, X., Wu, X., Fan, J., Elmagarmid, A. K., and Aref, W. G. 2004. Exploring video content structure for hierarchical summarization. Multimed. Syst. 10, 2, 98--115.

Collaborative Colleagues:
Zi Huang: colleagues
Heng Tao Shen: colleagues
Jie Shao: colleagues
Xiaofang Zhou: colleagues
Bin Cui: colleagues