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Cross-Domain Multi-Event Tracking via CO-PMHT

Published: 04 July 2014 Publication History

Abstract

With the massive growth of events on the Internet, efficient organization and monitoring of events becomes a practical challenge. To deal with this problem, we propose a novel CO-PMHT (CO-Probabilistic Multi-Hypothesis Tracking) algorithm for cross-domain multi-event tracking to obtain their informative summary details and evolutionary trends over time. We collect a large-scale dataset by searching keywords on two domains (Gooogle News and Flickr) and downloading both images and textual content for an event. Given the input data, our algorithm can track multiple events in the two domains collaboratively and boost the tracking performance. Specifically, the bridge between two domains is a semantic posterior probability, that avoids the domain gap. After tracking, we can visualize the whole evolutionary process of the event over time and mine the semantic topics of each event for deep understanding and event prediction. The extensive experimental evaluations on the collected dataset well demonstrate the effectiveness of the proposed algorithm for cross-domain multi-event tracking.

References

[1]
J. Allan. 2002. Topic Detection and Tracking: Event Based Information Retrieval. Kluwer Academic Press.
[2]
Y. Bar-Shalom, F. Daum, and J. Huang. 2009. The probabilistic data association filter. IEEE Control Syst. Mag. 29, 6, 82--100.
[3]
D. M. Blei, A. Y. Ng, and M. I. Jordan. 2003. Latent dirichlet allocation. J. Mach. Learn. Res. 3, 3--4, 993--1022.
[4]
H. L. Chieu and Y. K. Lee. 2004. Query based event extraction along a timeline. In Proceedings of the 27th Annual ACM SIGIR International Conference on Research and Development in Information Retrieval.
[5]
I. Cox and S. Hingorani. 1979. An efficient implementation of reid's multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking. IEEE Trans. Pattern Anal. Mach. Intell. 24, 6, 843--854.
[6]
A. Dempster, N. Laird, and D. Rubin. 1977. Maximum likelihood from incomplete data via the em algorithm. J. Royal Statist. Soc. B39, 1, 1--38.
[7]
P. Duygulu, J. Y. Pan, and D. A. Forsyth. 2005. Towards auto-documentary: Tracking the evolution of news stories. In Proceedings of the 12th Annual ACM International Conference on Multimedia (Multimedia'05). 820--827.
[8]
J. R. Kender and M. R. Naphade. 2005. Visual concepts for news story tracking: Analyzing and exploiting the nist trecvid video annotation experiment. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). 1174--1181.
[9]
Z. Knan, T. Balch, and F. Dellaert. 2005. MCMC-based particle filtering for tracking a variable number of interacting targets. IEEE Trans. Pattern Anal. Mach. Intell. 27, 11, 82--100.
[10]
S. Kullback and R. A. Leibler. 1951. On information and sufficiency. Ann. Math. Statist. 22, 1, 79--86.
[11]
R. Kumar, U. Mahadevan, and D. Sivakumar. 2004. A graph-theoretic approach to extract storylines from search results. In Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
[12]
F. Lin and C.-H. Liang. 2008. Storyline-based summarization for news topic retrospection. Decis. Support Syst. 45, 4, 473--490.
[13]
L. Liu, L. Wang, and X. Liu. 2011. In defense of soft assignment coding. In Proceedings of the IEEE International Conference on Computer Vision (ICCV'11). 2486--2493.
[14]
S. Liu, S. Yan, T. Zhang, C. Xu, J. Liu, and H. Lu. 2012. Weakly-supervised graph propagation towards collective image parsing. IEEE Trans. Multimedia 14, 2, 361--373.
[15]
J. Makkonen, H. A. Myka, and M. Salmenkivi. 2004. Simple semantics in topic detection and tracking. Inf. Retr. 7, 3--4, 347--368.
[16]
G. Salton and C. Buckley. 1988. Term-weighting approaches in automatic text retrieval. Inf. Process. Manag. 24, 5, 513--523.
[17]
G. Salton, A. Wong, and C. S. Yang. 1975. A vector space model for automatic indexing. Comm. ACM 18, 1, 613--620.
[18]
H. Shitrit, J. Berclaz, F. Fleuret, and P. Fua. 2011. Tracking multiple people under global appearance constraints. In Proceedings of the IEEE International Conference on Computer Vision (ICCV'11). 1--8.
[19]
R. L. Streit and T. E. Luginbuhl. 1995. Probabilistic multi-hypothesis tracking. Tech. rep. NUWC-NPT 10, 428, Naval Undersea Warfare Center, Newport, RI.
[20]
X. Wu, C.-W. Ngo, and A. G. Hauptmann. 2008. Multimodal news story clustering with pairwise visual near-duplicate constraint. IEEE Trans. Multimedia 10, 4, 188--199.
[21]
L. Xie, L. S. Kennedy, S.-F. Chang, A. Divakaran, H. Sun, and C.-Y. Lin. 2004. Discovering meaningful multimedia patterns with audio-visual concepts and associated text. In Proceedings of the International Conference on Image Processing (ICIP'04). 2383--2386.
[22]
J. Xing, H. Ai, and S. Lao. 2009. Multi-object tracking through occlusions by local tracklets filtering and global tracklets association with detection responses. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'09). 1--8.
[23]
Y. Yang, J. Carbonell, R. Brown, T. Pierce, B. Archibald, and X. Liu. 1999. Learning approaches for detecting and tracking news events. IEEE Intell. Syst. Appl. Intell. Inf. Retr. 14, 4, 32--43.
[24]
Q. Yu and G. Medioni. 2009. Multiple-target tracking by spatio-temporal monte carlo markov chain data association. IEEE Trans. Pattern Anal. Mach. Intell. 31, 12, 1--110.
[25]
Y. Zhai and M. Shah. 2005. Tracking news stories across different sources. In Proceedings of the 13th Annual ACM International Conference on Multimedia (Multimedia'05). 2--10.
[26]
T. Zhang, B. Ghanem, S. Liu, and N. Ahuja. 2012a. Low-rank sparse learning for robust visual tracking. In Proceedings of the 12th European Conference on Computer Vision (ECCV'12).
[27]
T. Zhang, C. Xu, G. Zhu, S. Liu, and H. Lu. 2012b. A generic framework for video annotation via semi-supervised learning. IEEE Trans. Multimedia 14, 4, 1206--1219.
[28]
T. Zhang, B. Ghanem, S. Liu, and N. Ahuja. 2012c. Robust visual tracking via multi-task sparse learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'12). 2042--2049.
[29]
T. Zhang, B. Ghanem, S. Liu, and N. Ahuja. 2013a. Robust visual tracking via structured multi-task sparse learning. Int. J. Comput. Vis. 101, 2, 367--383.
[30]
T. Zhang, B. Ghanem, S. Liu, C. Xu, and N. Ahuja. 2013b. Low-rank sparse coding for image classification. In Proceedings of the IEEE International Conference on Computer Vision (ICCV'13).
[31]
T. Zhang, S. Liu, C. Xu, and H. Lu. 2013c. Mining semantic context information for intelligent video surveillance of traffic scenes. IEEE Trans. Industr. Informat. 9, 1, 149--160.

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cover image ACM Transactions on Multimedia Computing, Communications, and Applications
ACM Transactions on Multimedia Computing, Communications, and Applications  Volume 10, Issue 4
June 2014
132 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/2656131
Issue’s Table of Contents
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 04 July 2014
Accepted: 01 March 2014
Revised: 01 March 2014
Received: 01 July 2013
Published in TOMM Volume 10, Issue 4

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Author Tags

  1. CO-PMHT
  2. Cross-domain
  3. PMHT
  4. multi-event tracking
  5. multi-modality

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  • (2022)Multi-feature, multi-modal, and multi-source social event detectionInformation Fusion10.1016/j.inffus.2021.10.01379:C(279-308)Online publication date: 1-Mar-2022
  • (2020)Knowledge-Based Topic Model for Multi-Modal Social Event AnalysisIEEE Transactions on Multimedia10.1109/TMM.2019.295119422:8(2098-2110)Online publication date: Aug-2020
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