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Journal of Electronic Imaging

Probabilistic latent semantic analysis for dynamic textures recognition and localization
Author(s): Yongxiong Wang; Shiqiang Hu
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Paper 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.

Paper Details

Date Published: 13 November 2014
PDF: 11 pages
J. Electron. Imaging. 23(6) 063006 doi: 10.1117/1.JEI.23.6.063006
Published in: Journal of Electronic Imaging Volume 23, Issue 6
Show Author Affiliations
Yongxiong Wang, Shanghai Jiao Tong Univ. (China)
Shiqiang Hu, Shanghai Jiao Tong Univ. (China)

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