Machine Learning
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multimedia Systems - Special section on video libraries
A mid-level representation framework for semantic sports video analysis
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Proceedings of the 12th annual ACM international conference on Multimedia
Motion pattern-based video classification and retrieval
EURASIP Journal on Applied Signal Processing
IEEE Transactions on Image Processing
A unified approach to shot change detection and camera motion characterization
IEEE Transactions on Circuits and Systems for Video Technology
Rapid estimation of camera motion from compressed video with application to video annotation
IEEE Transactions on Circuits and Systems for Video Technology
MPEG-7 visual motion descriptors
IEEE Transactions on Circuits and Systems for Video Technology
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Motion information is a powerful cue for visual perception. In the context of video indexing and retrieval, motion content serves as a useful source for compact video representation. There has been a lot of literature about parametric motion models. However, it is hard to secure a proper parametric assumption in a wide range of video scenarios. Diverse camera shots and frequent occurrences of bad optical flow estimation motivate us to develop nonparametric motion models. In this paper, we employ the mean shift procedure to propose a novel nonparametric motion representation. With this compact representation, various motion characterization tasks can be achieved by machine learning. Such a learning mechanism can not only capture the domain-independent parametric constraints, but also acquire the domain-dependent knowledge to tolerate the influence of bad dense optical flow vectors or block-based MPEG motion vector fields (MVF). The proposed nonparametric motion model has been applied to camera motion pattern classification on 23191 MVF extracted from MPEG-7 dataset.