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We report on our system used in the TRECVID 2013 Multimedia Event Detection (MED) and Multimedia Event Recounting (MER) tasks. For MED, it consists of four main steps: extracting features, representing features, training detectors and fusion. In the feature extraction part, we extract more than 10 low-level, high-level, and text features. Those features are then represented in three different ways which are spatial bag-of words, Gaussian Mixture Model Super Vectors (GMM) and Fisher Vectors. In the detector training and fusion, two classifiers and weighted double fusion method are employed. The official evaluation results show that our MED full systems achieve the best scores on Ah-Hoc EK10 and EK0, our audio systems achieve the best scores in EK100 and EK10 for both Pre-specified and Ad-Hoc tasks. Our MER system utilizes a subset of features and detection results from the MED system from which the recounting is generated.

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.