Abstract
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.
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References
Hofmann, T.: Unsupervised Learning by Probabilistic Latent Semantic Analysis. Machine Learning 42, 177–196 (2001)
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet Allocation. The Journal of Machine Learning Research 3, 993–1022 (2003)
Sivic, J., Russell, B., Efros, A., Zisserman, A., Freeman, W.: Discovering objects and their location in images. In: ICCV (2005)
Bosch, A., Zisserman, A., Munoz, X.: Scene classification using a hybrid generative/discriminative approach. TPAMI 30, 712–727 (2008)
Carlos Niebles, J., Wang, H., Li, F.F.: Unsupervsied Learning of Human Action Categories Using Spatial-Temporal Words. IJCV 42, 993–1022 (2008)
Savarese, S., DelPozo, A., Niebles, J., Fei-Fei, L.: Spatial-temporal correlations for unsupervised action classification. In: WMCV (2008)
Xu, J., Ye, G., Wang, Y., Herman, G., Zhang, B., Yang, J.: Incremental EM for Probabilistic Latent Semantic Analysis on Human Action Recognition. In: AVSS (2009)
Chou, T., Chen, M.: Using incremental PLSI for threshold-resilient online event analysis. IEEE Transactions on Knowledge and Data Engineering 20, 289 (2008)
AlSumait, L., Barbará, D., Domeniconi, C.: Online LDA: Adaptive Topic Model for Mining Text Streams with Application on Topic Detection and Tracking. In: ICDM (2008)
Chien, J., Wu, M.: Adaptive Bayesian latent semantic analysis. IEEE Transactions on Audio, Speech, and Language Processing 16, 198–207 (2008)
Hamilton, J.: A quasi-Bayesian approach to estimating parameters for mixtures of normal distributions. Journal of Business & Economic Statistics 9, 27–39 (1991)
Vailaya, A., Figueiredo, M., Jain, A., Zhang, H., Technol, A., Alto, P.: Image classification for content-based indexing. TIP 10, 117–130 (2001)
Wu, J., Rehg, J.: Where am I: Place instance and category recognition using spatial PACT. In: CVPR (2008)
Gupta, P., Arrabolu, S., Brown, M., Savarese, S.: Video Scene Categorization by 3D Hierarchical Histogram Matching. In: ICCV (2009)
Vogel, J., Schiele, B.: Natural scene retrieval based on a semantic modeling step. LNCS, pp. 207–215. Springer, Heidelberg (2004)
Fei-Fei, L., Perona, P.: A Bayesian hierarchical model for learning natural scene categories. In: CVPR (2005)
Quattoni, A., Torralb, A.: Recognizing Indoor Scenes. In: CVPR (2009)
Li, L., Socher, R., Fei-Fei, L.: Towards Total Scene Understanding Classification, Annotation and Segmentation in an Automatic Framework. In: CVPR (2009)
Yilmaz, A., Shah, M.: Recognizing Human Actions in Videos Acquired by Uncalibrated Moving Cameras. In: ICCV (2005)
Little, J., Boyd, J.: Recognizing people by their gait: the shape of motion. Journal of Computer Vision Research 1, 1–32 (1998)
Ikizler, N., Forsyth, D.: Searching Video for Complex Activities with Finite State Models. In: ICCV, vol. 3 (2007)
Pruteanu-Malinici, I., Carin, L.: Infinite hidden markov models for unusual-event detection in video. TIP 17, 811–822 (2008)
Wang, Y., Mori, G.: Max-margin hidden conditional random fields for human action recognition. In: CVPR (2009)
Dollar, P., Vincent, R., Garrison, C.: Behavior recognition via sparse spatio-temporal features. In: VS-PETS (2005)
Ke, Y., Sukthankar, R., Hebert, M.: Efficient visual event detection using volumetric features. In: ICCV, vol. 1 (2005)
Yeffet, L., Wolf, L.: Local Trinary Patterns for Human Action Recognition. In: ICCV (2009)
Gilbert, A., Illingworth, J., Bowden, R.: Fast Realistic Multi-Action Recognition using Mined Dense Spatio-temporal. In: ICCV (2009)
Dempster, A., Laird, N., Rubin, D., et al.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society 39, 1–38 (1977)
Papoulis, A., Pillai, S.U.: Probability, Random Variables, and Stochastic Processes. McGraw-Hill, New York (2002)
Sato, M.: Convergence of on-line EM algorithm. ICONIP 1, 476–481 (2000)
Fergus, R., Fei-Fei, L., Torralba, A.: ICCV 2005 short course on Object Recognition (2005), http://people.csail.mit.edu/fergus/iccv2005/bagwords.html
Google: Developer’s Guide to Picasa Web Albums Data API (2009), http://code.google.com/apis/picasaweb/overview.html
Lowe, D.: Distinctive image features from scale-invariant keypoints. IJCV 60, 91–110 (2004)
Oliva, A., Torralba, A.: Modeling the shape of the scene: A holistic representation of the spatial envelope. IJCV 42, 145–175 (2001)
Horster, E., Lienhart, R., Slaney, M.: Continuous visual vocabulary modelsfor plsa-based scene recognition. In: CIVR (2008)
Schuldt, C., Laptev, I., Caputo, B.: Recognizing human actions: A local svm approach. In: CVPR (2004)
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Xu, J., Ye, G., Wang, Y., Wang, W., Yang, J. (2011). Online Learning for PLSA-Based Visual Recognition. In: Kimmel, R., Klette, R., Sugimoto, A. (eds) Computer Vision – ACCV 2010. ACCV 2010. Lecture Notes in Computer Science, vol 6493. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19309-5_8
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DOI: https://doi.org/10.1007/978-3-642-19309-5_8
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