A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Incorporating prior knowledge with weighted margin support vector machines
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Free viewpoint action recognition using motion history volumes
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
A 3-dimensional sift descriptor and its application to action recognition
Proceedings of the 15th international conference on Multimedia
Human Activity Recognition with Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Making action recognition robust to occlusions and viewpoint changes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
A new pose-based representation for recognizing actions from multiple cameras
Computer Vision and Image Understanding
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Double fusion for multimedia event detection
MMM'12 Proceedings of the 18th international conference on Advances in Multimedia Modeling
Multitraining Support Vector Machine for Image Retrieval
IEEE Transactions on Image Processing
Multimodal feature fusion for robust event detection in web videos
CVPR '12 Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Ensemble of exemplar-SVMs for object detection and beyond
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
Learning equivariant structured output SVM regressors
ICCV '11 Proceedings of the 2011 International Conference on Computer Vision
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We propose a new ensemble-based classifier for multi-source human action recognition called Multi-Max-Margin Support Vector Machine (MMM-SVM). This ensemble method incorporates the decision values of multiple sources and makes an informed final prediction by merging multi-source feature's intrinsic decision strength. Experiments performed on the benchmark IXMAS multi-view dataset (Weinland [1]) demonstrate that the performance of our multi-view system can further improve the accuracy over single view by 3-13% and consistently outperform the direct-concatenation method. We further apply this ensemble technique for combining the decision values of contextual and motion information in the UCF Sports dataset (Liu, 2009 [2]) and the results are comparable to the state-of-the-art, which exhibits our algorithm's potential for further extension in other areas of feature fusion problems.