Retrieving video data via motion tracks of content symbols
CIKM '97 Proceedings of the sixth international conference on Information and knowledge management
Motion based retrieval of dynamic objects in videos
Proceedings of the 12th annual ACM international conference on Multimedia
Automatic identification of digital video based on shot-level sequence matching
Proceedings of the 13th annual ACM international conference on Multimedia
Detection of video sequences using compact signatures
ACM Transactions on Information Systems (TOIS)
Real-time human action recognition by luminance field trajectory analysis
MM '08 Proceedings of the 16th ACM international conference on Multimedia
V-QBE: video database retrieval by means of example motion of objects
ICMCS '96 Proceedings of the 1996 International Conference on Multimedia Computing and Systems
Content-Based Copy Retrieval Using Distortion-Based Probabilistic Similarity Search
IEEE Transactions on Multimedia
Models for motion-based video indexing and retrieval
IEEE Transactions on Image Processing
A fully automated content-based video search engine supporting spatiotemporal queries
IEEE Transactions on Circuits and Systems for Video Technology
MPEG-7 visual motion descriptors
IEEE Transactions on Circuits and Systems for Video Technology
Proceedings of the ACM International Conference on Image and Video Retrieval
A fast video copy detection approach by dynamic programming
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
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In this paper, we propose the use of motion tracks to generate video features, which are applied to perform video retrieval. We simply use the location of the object's center-of-gravity and its velocity across consecutive video frames to form motion flows, which are then recordedland stored in a video database. In the video retrieval phase, we propose ithe use of n-gram matching strategy to execute the video retrieval task. The retrieval process can be Iriggered query-by-example. We identify corresponding query and index contents accurately in order to detect "similar" videos. Experiments are carried out on CMU datahase. All empirical comparison over the state-of-the-art dynamic programming techniques is encouraging and demonstrates the advantage and feasibility of the n-gram melhod.