Carnegie Mellon University
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Informedia @ TRECVID 2011

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journal contribution
posted on 2011-12-01, 00:00 authored by Lei Bao, Shoou-I Yu, Zhen-Zhong Lan, Arnold Overwijk, Qin Jin, Brian Langner, Michael Garbus, Susanne Burger, Florian MetzeFlorian Metze, Alexander Hauptmann

The Informedia group participated in three tasks this year, including: Multimedia Event Detection (MED), Semantic Indexing (SIN) and Surveillance Event Detection. Generally, all of these tasks consist of three main steps: extracting feature, training detector and fusing. In the feature extraction part, we extracted a lot of low-level features, high-level features and text features. Especially, we used the Spatial-Pyramid Matching technique to represent the low-level visual local features, such as SIFT and MoSIFT, which describe the location information of feature points. In the detector training part, besides the traditional SVM, we proposed a Sequential Boosting SVM classifier to deal with the large-scale unbalance classification problem. In the fusion part, to take the advantages from different features, we tried three different fusion methods: early fusion, late fusion and double fusion. Double fusion is a combination of early fusion and late fusion. The experimental results demonstrated that double fusion is consistently better, or at least comparable than early fusion and late fusion.




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