TRECVID 2007 High-Level Feature Extraction By MCG-ICT-CAS
We participated in the high-level feature extraction task in TRECVID 2007. This paper describes the details of our system for the task. For feature extraction, we propose an EMD-based bag-of-feature method to exploit visual/spatial information, and utilize WordNet to expand semantic meanings of text to boost up the generalization of detectors. We also explore audio features and extract the motion cues in compressed domain for detecting concepts highly associated with audio/motion. We use Ordered Weighted Average (OWA) fusion method to combine the SVM-based multi-modal concept detection results. Experiment results show that our methods are effective.
MSRA-USTC-SJTU at TRECVID 2007: High-Level Feature Extraction and Search
This paper describes the MSRA-USTC-SJTU experiments for TRECVID 2007. We performed the experiments in high-level feature extraction and automatic search tasks. For high-level feature extraction, we investigated the benefit of unlabeled data by semi-supervised learning, and the multi-layer (ML) multi-instance (MI) relation embedded in video by MLMI kernel, as well as the correlations between concepts by correlative multi-label learning. For automatic search, we fuse text, visual example, and concept-based models while using temporal consistency and face information for re-ranking and result refinement.
CLIPS at TRECVID : Shot Boundary Detection and Feature Detection
This paper presents the systems used by CLIPS-IMAG to perform the Shot Boundary Detection (SBD) task and the Feature Extraction (FE) task of the TRECvid workshop. Results obtained for the 2003 evaluation are presented. The CLIPS SBD system based on image difference with motion compensation and direct dissolve detection was second among 14 systems. This system gives control of the silence to noise ratio over a wide range of values and for an equal value of noise and silence (or recall and precision), the value is 12 % for all types of transitions. Detection of person X from speaker recognition alone was deceiving due to the small number of shots containing person X in the overall test collection (about 1/700) and the even small number in which person X was actually speaking (about 1/6000). Detection of person X from speech transcription performed much better but was still lower than other systems using also the image track for the detection.
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