- Shoou-I Yu, Zhongwen Xu, Duo Ding, Waito Sze, F. Vicente, Zhenzhong Lan, Yang Cai, Shourabh Rawat, Peter F. Schulam, Nisarga Markandaiah, Sohail Bahmani, A. Juárez, Wei Tong, Yi Yang, Susanne Burger, Florian Metze, Rita Singh, Bhiksha Raj, Richard Stern, Teruko Mitamura, Eric Nyberg, Lu Jiang, Qiang Chen, Lisa M. Brown, Ankur Datta, Quanfu Fan, Rogério Schmidt Feris, S. Yan, Alexander Hauptmann, Sharath Pankanti. 2012. Informedia @TRECVID 2012.. Abstract: In the first part of this report we describe our system and novel approaches used in the TRECVID 2012 Multimedia Event Detection (MED) and Multimedia Event Recounting (MER) tasks. A separate section of the report (SIN) details methods and results for the Semantic Indexing task. The final section (SED) describes our approaches and results on the Surveillance Event Detection task. Abstract We report on our system used in the TRECVID 2012 Multimedia Event Detection (MED) and Multimedia Event Recounting (MER) tasks. For MED, generally, it consists of three main steps: extracting features, training detectors and fusion. In the feature extraction part, we extract many low-level, high-level features and text features. Those features are then represented in three different ways which are spatial bag-of words with standard tiling, spatial bag-of-words with feature and event specific tiling and the Gaussian Mixture Model Super Vector. In the detector training and fusion, two classifiers and three fusion methods are employed. The results from both of the official sources and our internal evaluations show good performance of our system. For our MER system, it takes some of the features and detection results from the MED system from which the recount is then generated.
