Motion recovery for video content classification
ACM Transactions on Information Systems (TOIS) - Special issue on video information retrieval
IEEE Transactions on Pattern Analysis and Machine Intelligence
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ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 3 - Volume 3
A hybrid system for affine-invariant trajectory retrieval
Proceedings of the 6th ACM SIGMM international workshop on Multimedia information retrieval
A new motion histogram to index motion content in video segments
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Tandem repeats over the edit distance
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Efficient similar trajectory-based retrieval for moving objects in video databases
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Subtrajectory-based video indexing and retrieval
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Real-Time Motion Trajectory-Based Indexing and Retrieval of Video Sequences
IEEE Transactions on Multimedia
Models for motion-based video indexing and retrieval
IEEE Transactions on Image Processing
Optimal Gabor filters for texture segmentation
IEEE Transactions on Image Processing
MPEG-7 visual motion descriptors
IEEE Transactions on Circuits and Systems for Video Technology
Motion-based video retrieval by trajectory matching
IEEE Transactions on Circuits and Systems for Video Technology
Real-Time exact graph matching with application in human action recognition
HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
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This paper proposes a Gabor filter based representation of motion trajectory, for the purpose of motion-based video retrieval. We propose a spectro-temporal representation of the trajectory, which involves the process of detecting a set of salient points from the peaks (locally) of the Gabor filter responses. The change in trajectory direction is also represented by observing the clockwise or anti-clockwise change in direction at the salient points. The feature set (formed by the frequency, temporal location and turning direction at each salient point) provides a semantic representation of the trajectory. Our approach is a global trajectory representation where matching is performed based on edit distance and is shown to perform well even for partial trajectory matching. The system is tested using two benchmark databases of trajectories, as well as various hand-drawn and partial trajectories. We have also experimented on real world videos. Experimental results have shown better performance than existing systems based on Fourier descriptors, polynomial representation and two state-of-the-art methods of symbolic representations based on PCA and characteristics of movement.