A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
The Journal of Machine Learning Research
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Efficient Visual Event Detection Using Volumetric Features
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Beyond Tracking: Modelling Activity and Understanding Behaviour
International Journal of Computer Vision
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Infinite Hidden Markov Models for Unusual-Event Detection in Video
IEEE Transactions on Image Processing
Online learning for PLSA-based visual recognition
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
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Human action recognition is a significant task in automatic understanding systems for video surveillance. Probabilistic Latent Semantic Analysis (PLSA) model has been used to learn and recognize human actions in videos. Specifically, PLSA employs the expectation maximization (EM) algorithm for parameter estimation during the training. The EM algorithm is an iterative estimation scheme that is guaranteed to find a local maximum of the likelihood function. However its convergence usually takes a large number of iterations. For action recognition with large amount of training data, this would result in long training time. This paper presents an incremental version of EM to speed up the training of PLSA without sacrificing performance accuracy. The proposed algorithm is tested on two challenging human action datasets. Experimental results demonstrate that the proposed algorithm converges with fewer number of full passes compared with the batch EM algorithm. And the trained PLSA models achieve comparable or better recognition accuracies than those using batch EM training.