Volume 41 Issue 4
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ZHENG Zhaobao, ZHENG Hong. Fuzzy Image Classification with a Certain Degree[J]. Geomatics and Information Science of Wuhan University, 2016, 41(4): 482-486. DOI: 10.13203/j.whugis20150712
Citation: ZHENG Zhaobao, ZHENG Hong. Fuzzy Image Classification with a Certain Degree[J]. Geomatics and Information Science of Wuhan University, 2016, 41(4): 482-486. DOI: 10.13203/j.whugis20150712
shu

Fuzzy Image Classification with a Certain Degree

  • 1.

    School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China;

  • 2.

    School of Electronic Information, Wuhan University, Wuhan 430072, China;

  • 3.

    Shenzhen Institute, Wuhan University, Shenzhen 518000, China

Funds: Science and Technology Plan of Shenzhen, No.JCYJ20150422150029095.
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  • Received Date: November 29, 2015
  • Published Date: April 04, 2016
    Graphical Abstract
    • Abstract
  • We proposed a new image fuzzy classification method of correlation degree with a certain degree. The correlation degree basis points are obtained in each image category, and the certain degree of an image related to each category are calculated. The certain degree of an image is the weight of the corresponding image correlation degree, and the attribute of the image classification to be recognized is determined by the maximum criterion, which is the correlation degree with a weight. In this paper, to recognize the image category, the second criterion is characterized by the correlation degree with a certain degree is that the classification of the image to be recognized is determined with the principle of minimum distance between clustering centers. In several experiments with three categories of images, the experimental results which meet one of the two principles in the experiment show that the results of this method has certain advantages.
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    • References (7)
  • [1]
    Zheng Zhaobao, Pan Li, Zheng Hong. Application of Image Correlation Degree to Image Fuzzy Classification[J]. Geomatics and Information Science of Wuhan University, 2015, 40(5):574-577(郑肇葆,潘励,郑宏. 应用图像关联度的图像模糊分类[J]. 武汉大学学报·信息科学版,2015,40(5):574-577)
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    Ishibuchi H, Nakashima T. Effect of Rule Weights in Fuzzy Rule-Based Classification Systems[J]. IEEE Transactions on Fuzzy Systems,2001,9(4):506-515
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    Ishibuchi H,Nozaki K, Tanaka H. Distributed Representation of Fuzzy Rules and Its Application to Pattern Classification[J]. Fuzzy Sets and Systems,1992,52:21-32
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    Nozaki K, Ishibuchi H,Tanaka H. Adaptive Fuzzy Rule-based Classification Systems[J]. IEEE Transactions on Fuzzy Systems,1996,52(1):21-32
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    Zheng Zhaobao,Zheng Hong. Genetic Algorithm for Producing Texture "TUNED" Masks[J]. Pattern Recognition and Artificial Intelligence, 2001, 14(1):119-122(郑肇葆,郑宏. 产生纹理"Tuned"模板的遗传算法[J]. 模式识别与人工智能,2001,14(1):119-122)
    [6]
    Zheng Zhaobao, Pan Li, Yu Xin. Bayesian Networks for Producing Texture "Tuned" Masks[J]. Geomatics and Information Science of Wuhan University, 2006,31(4):304-307(郑肇葆,潘励,虞欣. 产生"Tuned"模板的Bayesian Networks方法[J]. 武汉大学学报·信息科学版,2006,31(4):304-307)
    [7]
    Zheng Zhaobao. Honey-bee Mating Optimization Algorithm for Producing Better "Tuned" Masks[J]. Geomatics and Information Science of Wuhan University, 2009,34(4):387-399(郑肇葆. 产生最佳"Tuned"模板的蜜蜂交配算法[J]. 武汉大学学报·信息科学版,2009,34(4):387-390)
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    1. 郑肇葆, 郑宏. 利用数据引力进行图像分类. 武汉大学学报(信息科学版). 2017(11): 1604-1607 .

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