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Probabilistic latent semantic analysis for dynamic textures recognition and localization

[+] Author Affiliations
Yong Wang

Shanghai Jiao Tong University, School of Aeronautics and Astronautics, Dongchuan Road, Minhang District, Shanghai 200240, China

Shiqiang Hu

Shanghai Jiao Tong University, School of Aeronautics and Astronautics, Dongchuan Road, Minhang District, Shanghai 200240, China

J. Electron. Imaging. 23(6), 063006 (Nov 13, 2014). doi:10.1117/1.JEI.23.6.063006
History: Received March 13, 2014; Revised July 20, 2014; Accepted October 3, 2014
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Abstract.  We present a framework for dynamic textures (DTs) recognition and localization by using a model developed in the text analysis literature: probabilistic latent semantic analysis (pLSA). The novelty is revealed in three aspects. First, chaotic feature vector is introduced and characterizes each pixel intensity series. Next, the pLSA model is employed to discover the topics by using the bag of words representation. Finally, the spatial layout of DTs can be found. Experimental results are conducted on the well-known DTs datasets. The results show that the proposed method can successfully build DTs models and achieve higher accuracies in DTs recognition and effectively localize DTs.

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Yong Wang and Shiqiang Hu
"Probabilistic latent semantic analysis for dynamic textures recognition and localization", J. Electron. Imaging. 23(6), 063006 (Nov 13, 2014). ; http://dx.doi.org/10.1117/1.JEI.23.6.063006


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