Probabilistic latent semantic analysis for dynamic textures recognition and localization

被引：0
|
作者：
Wang, Yong [1 ]
Hu, Shiqiang [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Sch Aeronaut & Astronaut, Shanghai 200240, Peoples R China
来源：
JOURNAL OF ELECTRONIC IMAGING | 2014年 / 23卷 / 06期
基金：
中国国家自然科学基金;
关键词：
dynamic textures recognition; dynamic textures localization; chaotic feature vector; probabilistic latent semantic analysis; bag of words;
D O I：
10.1117/1.JEI.23.6.063006
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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. (C) 2014 SPIE and IS&T
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页数：11
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