吉林大学学报(工学版) ›› 2012, Vol. 42 ›› Issue (增刊1): 231-235.

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Object classification algorithm based on improved PLSA

ZHAO Hong-wei1, CHEN Xiao1, LONG Man-li2, YUAN Shi-pei1   

  1. 1. Department of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2. School of Foreign Language Education, Jilin University, Changchun 130012, China
  • Received:2011-12-28 Online:2012-09-01 Published:2012-09-01

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Abstract: The feature of object is described by SIFT.According to Bag-of-words model,Codebook is make from the features representation.Finally,classify the object by method of PLSA.Due to the iteration of complex issue in the learning process of the PLSA classification,direct statistical method in Bayesian classifier replaces the maximum likelihood estimates in PLSA,Thus provide sufficient prior knowledge for PLSA and reduce the number of iterations in the learning process.Experimental results show that the proposed method is more effective than the traditional method for object detection,and that is valid and feasible.

Key words: computer application, sift local descriptor, bag-of-words, PLSA, Bayesian classifier, object classification

CLC Number: 

  • TP391
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