A probabilistic framework for semi-supervised clustering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Towards auto-documentary: tracking the evolution of news stories
Proceedings of the 12th annual ACM international conference on Multimedia
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Fast tracking of near-duplicate keyframes in broadcast domain with transitivity propagation
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The trecvid 2007 BBC rushes summarization evaluation pilot
Proceedings of the international workshop on TRECVID video summarization
Skimming rushes video using retake detection
Proceedings of the international workshop on TRECVID video summarization
Scene duplicate detection from videos based on trajectories of feature points
Proceedings of the international workshop on Workshop on multimedia information retrieval
Scalable mining of large video databases using copy detection
MM '08 Proceedings of the 16th ACM international conference on Multimedia
A distance measure for repeated takes of one scene
The Visual Computer: International Journal of Computer Graphics
Rushes Video Parsing Using Video Sequence Alignment
CBMI '09 Proceedings of the 2009 Seventh International Workshop on Content-Based Multimedia Indexing
Video copy detection by fast sequence matching
Proceedings of the ACM International Conference on Image and Video Retrieval
Detecting and clustering multiple takes of one scene
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Video clip matching using MPEG-7 descriptors and edit distance
CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
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The automatic detection of near duplicate video segments, such as multiple takes of a scene or different news video clips showing the same event, has received growing research interest in recent years. However, there is no agreed way of evaluating near duplicate detection algorithms. This makes it very hard to compare the performance of different algorithms, even if they are applied to the same data set. In this paper we have implemented several evaluation measures found in literature and we apply them to real algorithm outputs and a simulated result data set. We then calculate the correlation between the results obtained with the different measures in order to investigate whether they can be compared or not. The results show that the correlation between the measures is some cases quite low, and some measures are especially sensitive to certain types of deviations from the ground truth. However, a group of precision/recall type measures and two others are clearly correlated, though with moderate correlation coefficients. We also analyze the correlation between these measures and the subjective human judgment of the number of repeated segments in summary videos.