The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
International Journal of Computer Vision
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Face Verification Using GaborWavelets and AdaBoost
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
HMM-based Human Action Recognition Using Multiview Image Sequences
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Facial expression recognition based on Local Binary Patterns: A comprehensive study
Image and Vision Computing
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Online dictionary learning for sparse coding
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
More generality in efficient multiple kernel learning
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Distance Metric Learning for Large Margin Nearest Neighbor Classification
The Journal of Machine Learning Research
Human Action Recognition by Semilatent Topic Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Feature detector and descriptor evaluation in human action recognition
Proceedings of the ACM International Conference on Image and Video Retrieval
The quadratic-chi histogram distance family
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part II
Signal Processing
Proximal Methods for Hierarchical Sparse Coding
The Journal of Machine Learning Research
Gait recognition using Pose Kinematics and Pose Energy Image
Signal Processing
Recognition and segmentation of 3-d human action using HMM and multi-class adaboost
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part IV
Slow Feature Analysis for Human Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Boosting local binary pattern (LBP)-Based face recognition
SINOBIOMETRICS'04 Proceedings of the 5th Chinese conference on Advances in Biometric Person Authentication
Action recognition using context and appearance distribution features
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Fast Pedestrian Detection Using a Cascade of Boosted Covariance Features
IEEE Transactions on Circuits and Systems for Video Technology
HMDB: A large video database for human motion recognition
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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In this paper we propose a method of feature selection using the AdaBoost algorithm for action recognition. Instead of detecting spatio-temporal interest points and using a 'bag of features' approach, we use densely sampled descriptors, either 3D-SIFT or 3D-HOG, and select the most discriminative subset using the AdaBoost algorithm. We obtain maximal accuracy with just 200 of the 3217 possible raw 3D features from each video sequence. Using the extremely simple naive Bayes nearest-neighbor (NBNN) classifier with the most discriminative 3D-SIFT features, we obtain accuracies of: 92.7%, 99.4%, 92.3% and 38.1% on the KTH, Weizmann, IXMAS and HMDB51 datasets, respectively. We also observe that the errors are reasonably equitably distributed across the different action classes.