Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
International Journal of Computer Vision
The Journal of Machine Learning Research
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
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
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
Using Incremental PLSI for Threshold-Resilient Online Event Analysis
IEEE Transactions on Knowledge and Data Engineering
Scene Classification Using a Hybrid Generative/Discriminative Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words
International Journal of Computer Vision
Continuous visual vocabulary modelsfor pLSA-based scene recognition
CIVR '08 Proceedings of the 2008 international conference on Content-based image and video retrieval
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Spatial-Temporal correlatons for unsupervised action classification
WMVC '08 Proceedings of the 2008 IEEE Workshop on Motion and video Computing
Incremental EM for Probabilistic Latent Semantic Analysis on Human Action Recognition
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Adaptive Bayesian Latent Semantic Analysis
IEEE Transactions on Audio, Speech, and Language Processing
Image classification for content-based indexing
IEEE Transactions on Image Processing
Infinite Hidden Markov Models for Unusual-Event Detection in Video
IEEE Transactions on Image Processing
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Probabilistic Latent Semantic Analysis (PLSA) is one of the latent topic models and it has been successfully applied to visual recognition tasks. However, PLSA models have been learned mainly in batch learning, which can not handle data that arrives sequentially. In this paper, we propose a novel on-line learning algorithm for learning the parameters of PLSA. Our contributions are two-fold: (i) an on-line learning algorithm that learns the parameters of a PLSA model from incoming data; (ii) a codebook adaptation algorithm that can capture the full characteristics of all the features during the learning. Experimental results demonstrate that the proposed algorithm can handle sequentially arriving data that batch PLSA learning cannot cope with, and its performance is comparable with that of the batch PLSA learning on visual recognition.