Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
International Journal of Computer Vision
Towards optimal bag-of-features for object categorization and semantic video retrieval
Proceedings of the 6th ACM international conference on Image and video retrieval
Evaluating Color Descriptors for Object and Scene Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human detection using oriented histograms of flow and appearance
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Action recognition by dense trajectories
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Representations of Keypoint-Based Semantic Concept Detection: A Comprehensive Study
IEEE Transactions on Multimedia
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Detecting event in multimedia video has become a popular research topic. One of the most important clues to determine an event in video is its motion features. Currently, motion features are often extracted from the whole video using dense sampling strategy. However, this extraction method is computationally prohibitive when it comes to large scale video dataset. Moreover, video length may be very different, which makes it unreliable to compare the feature between videos. In this paper, we propose to use segment-based approach to extract motion feature. Basically, original videos are quantized into fixed-length segments for both training and testing, while still keep evaluation at video-level. Our approach has achieved promising results when applying for dense trajectory motion feature on TRECVID 2010 Multimedia Event Detection (MED) dataset. Combining with global and local features, our event detection system has comparable performance with other state-of-the-art MED systems, while the computational cost is significantly reduced.