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Volume 41 Issue 2
Feb.  2015
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Journal of Beijing University of Aeronautics and Astronautics  > 2015  >  41(2): 302-310.
ZHANG Xu, JIANG Jianguo, HONG Richang, et al. Accelerated image classification algorithm based on naive Bayes K-nearest neighbor[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(2): 302-310. doi: 10.13700/j.bh.1001-5965.2014.0471(in Chinese)
Citation: ZHANG Xu, JIANG Jianguo, HONG Richang, et al. Accelerated image classification algorithm based on naive Bayes K-nearest neighbor[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(2): 302-310. doi: 10.13700/j.bh.1001-5965.2014.0471(in Chinese)
shu

Accelerated image classification algorithm based on naive Bayes K-nearest neighbor

doi: 10.13700/j.bh.1001-5965.2014.0471
  • 1.

    School of Computer and Information, Hefei University of Technology, Hefei 230009, China

  • 2. Department of Computer, Army Officer Academy of PLA, Hefei 230031, China

  • Received Date: 28 Apr 2014
  • Publish Date: 20 Feb 2015
    Abstract
  • Naive Bayes nearest neighbor (NBNN) classification algorithm possesses merits of avoiding feature quantization and image-to-class distance measurement, but it faces limitation of slow speed and low classification accuracy. To address the problem, a naive Bayes K-nearest neighbor classification algorithm was presented, where K-nearest neighbor searched by fast library for approximate nearest neighbors(FLANN) was employed and the influence of background information was removed. In order to improve the running speed and reduce memory cost, feature selection was included for reducing feature number of test and training images. And an attempt was tried to balance the contradictory between classification accuracy and classification time by reducing feature number of test image and training images simultaneously. The algorithm retains merits of original NBNN algorithm and requires no parameter learning process. Experimental results verify the correctness and effectiveness of the algorithm.

     

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