Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing
Content-Based Audio Classification and Retrieval for Audiovisual Data Parsing
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
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IEEE MultiMedia
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HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
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SUTC '08 Proceedings of the 2008 IEEE International Conference on Sensor Networks, Ubiquitous, and Trustworthy Computing (sutc 2008)
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CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 04
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ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
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ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
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IEEE Transactions on Multimedia - Special issue on integration of context and content
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IEEE/ACM Transactions on Networking (TON)
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CCNC'09 Proceedings of the 6th IEEE Conference on Consumer Communications and Networking Conference
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Natural disasters, such as hurricanes, could have an enormous impact on society. The level of the public's preparedness could make a significant difference in the severity of casualty and damage inflicted by such storms. We present a prototype system to reach out to the public and improve their awareness of the potential dangers involved with such weather events. This web-based system aggregates H*Wind storm track and wind fields data along with relevant videos extracted from YouTube and displays it to the user using Google Earth. A content-based concept detection algorithm is used to extract the videos, which may describe the impact of the storm in relevant geographic locations. Using Hurricane Ike as a case study, the result demonstrates how some of the information collected and displayed by the system could have increased the awareness of the public and potentially helped prepare them better to the devastating storm.