Algorithms on strings, trees, and sequences: computer science and computational biology
Algorithms on strings, trees, and sequences: computer science and computational biology
The Complexity of Some Problems on Subsequences and Supersequences
Journal of the ACM (JACM)
MFCS '94 Proceedings of the 19th International Symposium on Mathematical Foundations of Computer Science 1994
Video Google: A Text Retrieval Approach to Object Matching in Videos
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Robust Real-Time Face Detection
International Journal of Computer Vision
Segmenting motion capture data into distinct behaviors
GI '04 Proceedings of the 2004 Graphics Interface Conference
Detecting Irregularities in Images and in Video
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Unsupervised Discovery of Action Classes
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A stochastic grammar of images
Foundations and Trends® in Computer Graphics and Vision
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Discovering multivariate motifs using subsequence density estimation and greedy mixture learning
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Efficient Subwindow Search: A Branch and Bound Framework for Object Localization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Online discovery and maintenance of time series motifs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
MOMI-cosegmentation: simultaneous segmentation of multiple objects among multiple images
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part I
Discriminative Video Pattern Search for Efficient Action Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient subwindow search with submodular score functions
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Scale invariant cosegmentation for image groups
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Action bank: A high-level representation of activity in video
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Learning latent temporal structure for complex event detection
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Unsupervised learning of event AND-OR grammar and semantics from video
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Learning spatiotemporal graphs of human activities
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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Unsupervised discovery of commonalities in images has recently attracted much interest due to the need to find correspondences in large amounts of visual data. A natural extension, and a relatively unexplored problem, is how to discover common semantic temporal patterns in videos. That is, given two or more videos, find the subsequences that contain similar visual content in an unsupervised manner. We call this problem Temporal Commonality Discovery (TCD). The naive exhaustive search approach to solve the TCD problem has a computational complexity quadratic with the length of each sequence, making it impractical for regular-length sequences. This paper proposes an efficient branch and bound (B&B) algorithm to tackle the TCD problem. We derive tight bounds for classical distances between temporal bag of words of two segments, including ℓ1, intersection and χ2. Using these bounds the B&B algorithm can efficiently find the global optimal solution. Our algorithm is general, and it can be applied to any feature that has been quantified into histograms. Experiments on finding common facial actions in video and human actions in motion capture data demonstrate the benefits of our approach. To the best of our knowledge, this is the first work that addresses unsupervised discovery of common events in videos.