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基于朴素贝叶斯K近邻的快速图像分类算法

张旭 蒋建国 洪日昌 杜跃

张旭, 蒋建国, 洪日昌, 等 . 基于朴素贝叶斯K近邻的快速图像分类算法[J]. 北京航空航天大学学报, 2015, 41(2): 302-310. doi: 10.13700/j.bh.1001-5965.2014.0471
引用本文: 张旭, 蒋建国, 洪日昌, 等 . 基于朴素贝叶斯K近邻的快速图像分类算法[J]. 北京航空航天大学学报, 2015, 41(2): 302-310. doi: 10.13700/j.bh.1001-5965.2014.0471
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)

基于朴素贝叶斯K近邻的快速图像分类算法

doi: 10.13700/j.bh.1001-5965.2014.0471
基金项目: 国家自然科学基金资助项目(61172164)
详细信息
    作者简介:

    张旭(1981—), 男, 安徽亳州人, 博士生, zhangxu21cn@163.com

    通讯作者:

    洪日昌(1981—), 男, 安徽黄山人, 教授, hongrc@hfut.edu.cn, 主要研究方向为多媒体内容分析、信息检索、多媒体问答.

  • 中图分类号: TP391

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

  • 摘要: 朴素贝叶斯最近邻(NBNN)分类算法具有非特征量化和图像-类别度量方式的优点,但算法运行速度较慢,分类正确率较低.针对此问题,提出一种朴素贝叶斯K近邻分类算法,基于快速近似最近邻(FLANN)搜索特征的K近邻用于分类决策并去除背景信息对分类性能的影响;为了进一步提高算法的运行速度及减少算法的内存开销,采用特征选择的方式分别减少测试图像和训练图像集的特征数目,并尝试同时减少测试图像和训练图像集中的特征数目平衡分类正确率与分类时间之间的矛盾.该算法保留了原始NBNN算法的优点,无需参数学习的过程,实验结果验证了算法的正确性和有效性.

     

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出版历程
  • 收稿日期:  2014-04-28
  • 网络出版日期:  2015-02-20

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