Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Unsupervised Discovery of Action Classes
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
MILES: Multiple-Instance Learning via Embedded Instance Selection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Observing Human-Object Interactions: Using Spatial and Functional Compatibility for Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey on vision-based human action recognition
Image and Vision Computing
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Weakly Supervised Learning of Interactions between Humans and Objects
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human action recognition by learning bases of action attributes and parts
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Knives are picked before slices are cut: recognition through activity sequence analysis
Proceedings of the 5th international workshop on Multimedia for cooking & eating activities
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We propose a multi-cue based approach for recognizing human actions in still images, where relevant object regions are discovered and utilized in a weakly supervised manner. Our approach does not require any explicitly trained object detector or part/attribute annotation. Instead, a multiple instance learning approach is used over sets of object hypotheses in order to represent objects relevant to the actions. We test our method on the extensive Stanford 40 Actions dataset [1] and achieve significant performance gain compared to the state-of-the-art. Our results show that using multiple object hypotheses within multiple instance learning is effective for human action recognition in still images and such an object representation is suitable for using in conjunction with other visual features.