JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2015, Vol. 45 ›› Issue (5): 13-21.doi: 10.6040/j.issn.1672-3961.2.2015.168

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Image classification algorithm based on minimax probability machine with regularized probability density concensus

WANG Xiaochu1, WANG Shitong1, BAO Fang2   

  1. 1. School of Digital Media, Jiangnan University, Wuxi 214122, Jiangsu, China;
    2. Department of Computer Science, Jiangyin Pdyteehnie College, Jiangyin 214405, Jiangsu, China
  • Received:2015-05-18 Revised:2015-09-05 Online:2018-10-20 Published:2015-05-18

Abstract: In order to solve image classification problem of which the images contained labeled and unlabeled samples, this research proposed an image classification algorithm based on minimax probability machine regularized by probability density concensus(called PDMPM). The distribution of the image samples in the hyperplane was estimated by using the probability density estimation function and probability density estimation constrained item was got by minimizing the distribution of the labeled and unlabeled samples. The probability density estimation constraint item was integrated into the nonlinear minimax maximum probability machine and used for image classification. The accuracy of the proposed algorithm was increased by 3.99% compared with Gaussian kernel minimax probability machine in the test of Yale face database, five of the Caltech 101 database and ten of Fifteen Scene Categories Dataset. Experimental results indicated that the method made full use of the distribution information of unlabeled image samples and made the image classification hyperplane more accurate.

Key words: classification hyperplane, probability density estimation, image classification, unlabeled sample, minimax probability machine

CLC Number: 

  • TP391
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