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INTEGRATING MULTIMEDIA SEMANTIC CONTENT ANALYSIS OF YOUTUBE VIDEOS WITH HURRICANE WIND ANALYSIS FOR PUBLIC SITUATION AWARENESS AND OUTREACH

GUY RAVITZ

Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA

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MEI-LING SHYU

Department of Electrical and Computer Engineering, University of Miami, Coral Gables, FL, USA

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, and

MARK D. POWELL

NOAA-AOML-HRD, Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA

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https://doi.org/10.1142/S0218194010004670Cited by:3 (Source: Crossref)

Abstract

Natural disasters, such as hurricanes, have had an enormous social and economical impact on society in the United State and around the world for many years. With the goal of preventing, diverting, or weakening the destructive forces of tropical cyclones, the preparedness of the public plays a major role in the magnitude of inflicted damage due to these storms. Acknowledging the captivating power of social networking and Web 2.0 over society, we present a prototype system which integrates meteorological data along with user generated content with the aim of improving public response by increasing their situational awareness due to such natural threats. The proposed system aggregates storm track and wind analysis data from the existing H*Wind system along with videos taken from YouTube and presents it to the user in Google Earth. A content-based concept detection mechanism is used to evaluate the relevance of the extracted YouTube videos to the storm of interest. The proposed system demonstrates the potential public benefit resulting from the integration of the areas of multimedia content analysis, Web 2.0, and meteorology.

Keywords:

References

J. L. Bevenet al., AMS Monthly Weather Review 136, 1109 (2008), DOI: 10.1175/2007MWR2074.1. Crossref, Web of Science, Google Scholar
Wikipedia, http://en.wikipedia.org/wiki/2008_Atlantic_hurricane_season . Google Scholar
M. L. Dolfman, S. F. Wasswe and B. Bergman, Monthly Labor Review 130(6), 3 (2007). Google Scholar
M. D. Powell and T. A. Reinhold, Bull. Amer. Meteo. Soc. 88(4), 513 (2007), DOI: 10.1175/BAMS-88-4-513. Crossref, Web of Science, Google Scholar
S. Murugesan, IT Professional 9(4), 34 (2007), DOI: 10.1109/MITP.2007.78. Crossref, Google Scholar
A. Turatiet al., Analyzing user actions within a Web 2.0 portal to improve a collaborative filtering recommendation system, IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technologies (2009) pp. 65–68. Google Scholar
YouTube, http://www.youtube.com/ . Google Scholar
S. Boll, IEEE Multimedia 14(1), 9 (2007), DOI: 10.1109/MMUL.2007.17. Crossref, Web of Science, Google Scholar
E. Changet al., Panel discussion: Web 2.0 and multimedia: Challenges, hype, synergy, Proceedings of ACM Multimedia (2006) p. 752. Google Scholar
M. Kimet al., Face tracking and recognition with visual constraints in real-world videos, IEEE Conference on Computer Vision and Pattern Recognition (2008) pp. 1–8. Google Scholar
M. Chaet al., IEEE/ACM Transaction on Networking 17(5), 1357 (2009), DOI: 10.1109/TNET.2008.2011358. Web of Science, Google Scholar
X. Cheng, C. Dale and J. Liu, Statistics and social network of YouTube videos, 16th International Workshop on Quality of Service (2008) pp. 229–238. Google Scholar
J. Paolillo, Structure and network in the youtube core, 41st Annual Hawaii International Conference on System Sciences (2008) pp. 156–156. Google Scholar
M. Soraya, M. Zamani and A. Abhari, Modeling of multimedia files on the Web 2.0, Canadian Conference on Electrical and Computer Engineering (2008) pp. 1387–1392. Google Scholar
A. Karpenko and P. Aarabi, Tiny videos: Non-parametric content-based video retrieval and recognition, Tenth IEEE International Symposium on Multimedia (2008) pp. 619–624. Google Scholar
S. Liu, M. Zhu and Q. Zheng, Mining similarities for clustering Web video clips, International Conference on Computer Science and Software Engineering4 (2008) pp. 759–762. Google Scholar
X. Wuet al., IEEE Transactions on Multimedia 11(2), 196 (2009), DOI: 10.1109/TMM.2008.2009673. Crossref, Web of Science, Google Scholar
L. Begeja and P. Van Vleck, Contextual advertising for IPTV using automated metadata generation, 6th IEEE Consumer Communications and Networking Conference (2009) pp. 1–5. Google Scholar
S. Venkateshet al., Proceedings of the IEEE 96(4), 697 (2008), DOI: 10.1109/JPROC.2008.916378. Crossref, Web of Science, Google Scholar
A. F. Smeaton, P. Over and W. Kraaij, Evaluation campaigns and TRECVID, Proceedings of the 8th ACM International Workshop on Multimedia Information Retrieval (ACM Press, 2006) pp. 321–330. Google Scholar
L. Linet al., Video semantic concept discovery using multimodal-based association classification, IEEE International Conference on Multimedia and Expo (2007) pp. 859–862. Google Scholar
L. Lin et al. , Correlation-based video semantic concept detection using multiple correspondence analysis , IEEE International Symposium on Multimedia (ISM) ( 2008 ) . Google Scholar
L. Linet al., Effective feature space reduction with imbalanced data for semantic concept detection, IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (2008) pp. 262–269. Google Scholar
M. D. Powellet al., Journal of Wind Engineering and Industrial Aerodynamics 77(78), 53 (1998). Web of Science, Google Scholar
H*Wind, http://www.aoml.noaa.gov/hrd/data_sub/wind.html . Google Scholar
YouTube-API, http://code.google.com/apis/youtube/ . Google Scholar
T. Zhang and C.-C. Jay Kuo , Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing ( Kluwer Academic Publishers , 2001 ) . Crossref, Google Scholar
G. Ravitz, M.-L. Shyu and M. D. Powell, Integration of hurricane wind analysis and multimedia semantic content analysis for public outreach, IEEE International Conference on Information Reuse and Integration (2009) pp. 324–327. Google Scholar
I. H. Witten and E. Frank , Data Mining: Practical Machine Learning Tools and Techniques ( Morgan Kaufmann , 2005 ) . Google Scholar