13 November 2014 Probabilistic latent semantic analysis for dynamic textures recognition and localization
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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.
© 2014 SPIE and IS&T
Yongxiong Wang, Shiqiang Hu, "Probabilistic latent semantic analysis for dynamic textures recognition and localization," Journal of Electronic Imaging 23(6), 063006 (13 November 2014). https://doi.org/10.1117/1.JEI.23.6.063006 . Submission:
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