Algorithmic graph theory
A new approach to retrieve video by example video clip
MULTIMEDIA '99 Proceedings of the seventh ACM international conference on Multimedia (Part 2)
Content-based video similarity model
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
Motion-Based Video Representation for Scene Change Detection
International Journal of Computer Vision
Multimedia Systems - Special section on video libraries
Video partitioning by temporal slice coherency
IEEE Transactions on Circuits and Systems for Video Technology
Beyond topical similarity: a structural similarity measure for retrieving highly similar documents
Knowledge and Information Systems
An integrated approach to video retrieval
ADC '08 Proceedings of the nineteenth conference on Australasian database - Volume 75
A measure based on optimal matching in graph theory for document similarity
AIRS'04 Proceedings of the 2004 international conference on Asian Information Retrieval Technology
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In this paper, a novel approach for automatic matching, ranking and retrieval of video clips is proposed. Motivated by the maximal and optimal matching theories in graph analysis, a new similarity measure of video clips is defined based on the representation and modeling of bipartite graph. Four different factors: visual similarity, granularity, interference and temporal order of shots are taken into consideration for similarity ranking. These factors are progressively analyzed in the proposed approach. Maximal matching utilizes the granularity factor to efficiently filter false matches, while optimal matching takes into account the visual, granularity and interference factors for similarity measure. Dynamic programming is also formulated to quantitatively evaluate the temporal order of shots. The final similarity measure is based on the results of optimal matching and dynamic programming. Experimental results indicate that the proposed approach is effective and efficient in retrieving and ranking similar video clips.