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  2013, Vol. 26 Issue (12): 1146-1153    DOI:
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Image Hierarchical Representation Model Based on LDA
JIA Zhen-Hua, SIQING Ba-La
Department of Computer Science and Engineering, North China Institute of Aerospace Engineering, Langfang 065000

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Abstract  The existing image hierarchical representation methods are strict in feed-forward style, and therefore it is not able to solve problems like local ambiguities well. In this paper, a probabilistic model is proposed to learn and deduce all layers of the hierarchy together. Specifically, a recursive probabilistic decomposition process is taken into account, and a generative model based on latent Dirichlet allocation with pyramidal multilayer structure is derived. Two important properties of the proposed probabilistic model are demonstrated: adding an additional representation layer to improve the performance of the flat model and adopting a full Bayesian approach which is better than a feed-forward implementation of the model. Experimental results on a standard recognition dataset show that the proposed method outperforms the existing hierarchical approaches, and it improves the classification and the learning accuracy with better performance.
Key wordsImage Hierarchical Representation      Feed-Forward      Probabilistic Model      Latent Dirichlet Allocation (LDA)     
Received: 14 August 2012     
ZTFLH: TP 391  
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JIA Zhen-Hua
SIQING Ba-La
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JIA Zhen-Hua,SIQING Ba-La. Image Hierarchical Representation Model Based on LDA[J]. , 2013, 26(12): 1146-1153.
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http://manu46.magtech.com.cn/Jweb_prai/EN/      OR     http://manu46.magtech.com.cn/Jweb_prai/EN/Y2013/V26/I12/1146
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