The nature of statistical learning theory
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An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Recognizing planned multiperson action
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Using Pervasive Computing to Deliver Elder Care
IEEE Pervasive Computing
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Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Pedestrian Detection via Classification on Riemannian Manifolds
IEEE Transactions on Pattern Analysis and Machine Intelligence
Robust Face Recognition via Sparse Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Incremental spectral clustering by efficiently updating the eigen-system
Pattern Recognition
Human Action Recognition in Videos Using Kinematic Features and Multiple Instance Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action Recognition in Video by Covariance Matching of Silhouette Tunnels
SIBGRAPI '09 Proceedings of the 2009 XXII Brazilian Symposium on Computer Graphics and Image Processing
Discriminative semi-supervised feature selection via manifold regularization
IEEE Transactions on Neural Networks
Semi-Supervised Classification via Local Spline Regression
IEEE Transactions on Pattern Analysis and Machine Intelligence
Semi-Supervised Learning via Regularized Boosting Working on Multiple Semi-Supervised Assumptions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Action recognition in video by sparse representation on covariance manifolds of silhouette tunnels
ICPR'10 Proceedings of the 20th International conference on Recognizing patterns in signals, speech, images, and videos
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Computer Vision and Image Understanding
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IEEE Transactions on Knowledge and Data Engineering
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Recognizing human actions in a video sequence using small number of labeled data are very desirable in practical applications. In this paper an action recognition approach via Labeled Kernel Sparse Coding (LKSC) and sparse L"1 graph is proposed under the framework of semi-supervised learning framework. Inspired by the fact that kernel trick can capture the nonlinear similarity of features, we extend the sparse representation classifier (SRC) is extended to the empirical kernel projection space. By using the calculated sparse coding coefficients of both labeled and unlabeled samples as the graph weights, a sparse L"1 graph is established in a parameter-free manner. Some experiments are taken on Weizmann human action datasets and a movie sequence of a ballet dance, to investigate the performance of our proposed method, and the results show that it is insensitive to the only parameter @s, and can achieve high recognition rate when using some number of labeled samples.