Temporal Commonality Discovery
Temporal Commonality Discovery (TCD) ≡ Given two (or more) videos, discover common events in an unsupervised manner.
>> cd tcd_v1.2
- Change the variable pickEx for more examples.
Files included in tcd_v1.2.zip
- addpaths.m: Add required paths to Matlab.
- demoTCD_soft.m: Demo of TCD using soft-clustering
- demoTCD_hard.m: Demo of TCD using hard-clustering
- func/: Folder that contains main functions of TCD
- featkmeans/: A method for getting the histograms. Other clustering methods can be directly applied.
- Extend TCD with soft clustering and hard clustering
- Speedup TCD search using integral image
- Speedup multiple TCD search using queue elimination
- Add the main implementation of the TCD algorithm
- Add comparisons to the naive SW approach
- Add a demo for 8 synthetic examples, including detecting multiple commonalities and an application to video indexing
Thanks for using the software ☺
This software is free for use in research projects. If you publish results obtained using this software, please use this citation. Contributing back bugfixes and improvements is polite and encouraged. If you have any questions, feel free to contact Wen-Sheng Chu.
- Temporal Commonality Discovery (TCD)
- Unsupervised commonality discovery in images has recently attracted much interest (see an example of co-segmentation).
- In this study, we investigate a relatively unexplored problem to discover common semantic temporal patterns in videos.
- To the best of our knowledge, this is the first work that addresses unsupervised discovery of common events in videos.
- By interpreting the problem into a 2D search space, we propose a branch and bound (B&B) algorithm to efficiently find the global optimum.
- We derive tight bounds for classical distances between Temporal Bag of Words (TBoW) of two segments, including L1, intersection and Χ2.
- The framework is general and can be applied to any feature that has been quantified into histograms.
| Efficient subwindow search: A B&B framework for object localization |
C. Lampert, M. Blaschko, and T. Hofmann,
[ pdf ]
| Online discovery and maintenance of time series motifs |
A. Mueen and E. Keogh,
[ pdf ]