Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
A survey on vision-based human action recognition
Image and Vision Computing
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Actom sequence models for efficient action detection
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
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We introduce a new problem called coaction discovery: the task of discovering and segmenting the common actions (coactions) between videos that may contain several actions. This paper presents an approach for coaction discovery; the key idea of our approach is to compute an action proposal map for each video based jointly on dynamic object-motion and static appearance semantics, and unsupervisedly cluster each video into atomic action clips, called actoms. Subsequently, we use a temporally coherent discriminative clustering framework for extracting the coactions. We apply our coaction discovery approach to two datasets and demonstrate convincing and superior performance to three baseline methods.