The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.
Event Driven Web Video Summarization by Tag Localization and Key-Shot Identification
Abstract
With the explosive growth of web videos on the Internet, it becomes challenging to efficiently browse hundreds or even thousands of videos. When searching an event query, users are often bewildered by the vast quantity of web videos returned by search engines. Exploring such results will be time consuming and it will also degrade user experience. In this paper, we present an approach for event driven web video summarization by tag localization and key-shot mining. We first localize the tags that are associated with each video into its shots. Then, we estimate the relevance of the shots with respect to the event query by matching the shot-level tags with the query. After that, we identify a set of key-shots from the shots that have high relevance scores by exploring the repeated occurrence characteristic of key sub-events. Following the scheme in [6] and [22], we provide two types of summaries, i.e., threaded video skimming and visual-textual storyboard. Experiments are conducted on a corpus that contains 60 queries and more than 10 000 web videos. The evaluation demonstrates the effectiveness of the proposed approach.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Statistical Methods for Research Workers
(1945) · American Journal of Clinical Pathology · doi:10.1093/ajcp/15.11.552b · Cited by 10,880
Clustering by Passing Messages Between Data Points
Frey, B. J., & Dueck, D. (2007) · Science · doi:10.1126/science.1136800 · Cited by 6,758
NUS-WIDE: a real-world web image database from National University of Singapore
Chua, T.-S., Tang, J., Hong, R., Li, H., Luo, Z., & Zheng, Y. (2009) · Proceedings of the ACM International Conference on Image and Video Retrieval · doi:10.1145/1646396.1646452 · Cited by 3,025
Self-citationI tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Cha, M., Kwak, H., Rodriguez, P., Ahn, Y.-Y., & Moon, S. (2007) · Proceedings of the 7th ACM SIGCOMM conference on Internet measurement · doi:10.1145/1298306.1298309 · Cited by 1,576
Statistical Methods for Research Workers
F., W. T., & Fisher, R. A. (1971) · Biometrics · doi:10.2307/2528855 · Cited by 657
Multiple-Instance Active Learning
Settles, B., Craven, M. W., & Ray, S. (2007) · Cited by 489
Unified Video Annotation via Multigraph Learning
Wang, M., Hua, X.-S., Hong, R., Tang, J., Qi, G.-J., & Song, Y. (2009) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2009.2017400 · Cited by 441
Self-citationVisual Event Recognition in Videos by Learning from Web Data
Duan, L., Xu, D., Tsang, I. W.-H., & Luo, J. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2011.265 · Cited by 409
Event detection and analysis from video streams
Medioni, G., Cohen, I., Bremond, F., Hongeng, S., & Nevatia, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.946990 · Cited by 405
Cited by
TVSum: Summarizing web videos using titles
Song, Y., Vallmitjana, J., Stent, A., & Jaimes, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299154 · Cited by 623
Video Question Answering via Gradually Refined Attention over Appearance and Motion
Xu, D., Zhao, Z., Xiao, J., Wu, F., Zhang, H., He, X., & Zhuang, Y. (2017) · Proceedings of the 25th ACM international conference on Multimedia · doi:10.1145/3123266.3123427 · Cited by 429
Metadata
- Authors
- Wang, M., Hong, R., Li, G., Zha, Z.-J., Yan, S., & Chua, T.-S.
- Venue
- IEEE Transactions on Multimedia
- Published
- 2012-08
- Volume
- 14
- Issue
- 4
- Pages
- 975-985
- ISSN
- 1520-9210 (ISSN-L)
- 1941-0077
- OpenAlex
- W2095536970
- Cited by
- 263
- References
- 30
- FWCI
- 35.08
- Type
- journal article
- Language
- en
- Open access
- closed
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