Original Contribution: Stacked generalization
Neural Networks
Solving the multiple instance problem with axis-parallel rectangles
Artificial Intelligence
Segmenting film sequences using active surfaces
ICIP '97 Proceedings of the 1997 International Conference on Image Processing (ICIP '97) 3-Volume Set-Volume 1 - Volume 1
Probability Estimates for Multi-class Classification by Pairwise Coupling
The Journal of Machine Learning Research
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Space-Time Behavior Based Correlation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
ACM SIGGRAPH 2005 Papers
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Movie/Script: Alignment and Parsing of Video and Text Transcription
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
ISVC'11 Proceedings of the 7th international conference on Advances in visual computing - Volume Part II
Multiple instance learning for classification of human behavior observations
ACII'11 Proceedings of the 4th international conference on Affective computing and intelligent interaction - Volume Part I
Kernelized temporal cut for online temporal segmentation and recognition
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part III
Learning latent spatio-temporal compositional model for human action recognition
Proceedings of the 21st ACM international conference on Multimedia
Human action recognition with salient trajectories
Signal Processing
Object and Action Classification with Latent Window Parameters
International Journal of Computer Vision
Max-Margin Early Event Detectors
International Journal of Computer Vision
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In this paper, we present a framework for estimating what portions of videos are most discriminative for the task of action recognition. We explore the impact of the temporal cropping of training videos on the overall accuracy of an action recognition system, and we formalize what makes a set of croppings optimal. In addition, we present an algorithm to determine the best set of croppings for a dataset, and experimentally show that our approach increases the accuracy of various state-of-the-art action recognition techniques.