Representing Videos Using Mid-level Discriminative Patches

Arpit Jain, Abhinav Gupta, Mikel Rodriguez, Larry S. Davis; The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013, pp. 2571-2578

Abstract


representation for videos based on mid-level discriminative spatio-temporal patches. These spatio-temporal patches might correspond to a primitive human action, a semantic object, or perhaps a random but informative spatiotemporal patch in the video. What defines these spatiotemporal patches is their discriminative and representative properties. We automatically mine these patches from hundreds of training videos and experimentally demonstrate that these patches establish correspondence across videos and align the videos for label transfer techniques. Furthermore, these patches can be used as a discriminative vocabulary for action classification where they demonstrate stateof-the-art performance on UCF50 and Olympics datasets.

Related Material


[pdf]
[bibtex]
@InProceedings{Jain_2013_CVPR,
author = {Jain, Arpit and Gupta, Abhinav and Rodriguez, Mikel and Davis, Larry S.},
title = {Representing Videos Using Mid-level Discriminative Patches},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2013}
}