L. Cao, S.-F. Chang, N. Codella, C. Cotton, D. Ellis, L. Gong, M. Hill, G. Hua, J. Kender, M. Merler, Y. Mu, A. Natsev, J. R. Smith. IBM Research and Columbia University TRECVID-2011 Multimedia Event Detection (MED) System. In NIST TRECVID Workshop, Gaithersburg, MD, December 2011.
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The IBM Research/Columbia team investigated a novel range of low-level and high-level features and their combination for the TRECVID Multimedia Event Detection (MED) task. We submitted four runs exploring various methods of extraction, modeling and fusing of low-level features and hundreds of high-level semantic concepts. Our Run 1 developed event detection models utilizing Support Vector Machines (SVMs) trained from a large number of low-level features and was interesting in establishing the baseline performance for visual features from static video frames. Run 2 trained SVMs from classification scores generated by 780 visual, 113 action and 56 audio high-level semantic classi.ers and explored various temporal aggregation techniques. Run 2 was interesting in assessing performance based on different kinds of high-level semantic information. Run 3 fused the lowand high-level feature information and was interesting in providing insight into the complementarity of this information for detecting events. Run 4 fused all of these methods and explored a novel Scene Alignment Model (SAM) algorithm that utilized temporal information discretized by scene changes in the video
Shih-Fu Chang 
Yadong Mu
John_R. Smith
@InProceedings{NIST-TRECVID11:IBM-DVMM,
Author = {Cao, L. and Chang, S.-F. and Codella, N. and Cotton, C. and Ellis, D. and Gong, L. and Hill, M. and Hua, G. and Kender, J. and Merler, M. and Mu, Y. and Natsev, A. and Smith, J. R.},
Title = {IBM Research and Columbia University TRECVID-2011 Multimedia Event Detection (MED) System},
BookTitle = {NIST TRECVID Workshop},
Address = {Gaithersburg, MD},
Month = {December},
Year = {2011}
}
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