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Herb Leaves Recognition using Gray Level Co-occurrence Matrix and Five Distance-based Similarity Measures

机译:基于灰度共生矩阵和五种基于距离的相似性度量的药草叶识别

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Herb medicinal products derived from plants have long been considered as an alternative option for treating various diseases. In this paper, the feature extraction method used is Gray Level Co-occurrence Matrix (GLCM), while for its recognition using the metric calculations of Chebyshev, Cityblock, Minkowski, Canberra, and Euclidean distances. The method of determining the GLCM Analysis based on the texture analysis resulting from the extraction of this feature is Angular Second Moment, Contrast, Inverse Different Moment, Entropy as well as its Correlation. The recognition system used 10 leaf test images with GLCM method and Canberra distance resulted in the highest accuracy of 92.00%. While the use of 20 and 30 test data resulted in a recognition rate of 50.67% and 60.00%.
机译:长期以来,源自植物的草药产品一直被视为治疗各种疾病的替代选择。在本文中,使用的特征提取方法是灰度共生矩阵(GLCM),而使用Chebyshev,Cityblock,Minkowski,Canberra和Euclidean距离的度量计算进行识别。基于提取该特征而得到的纹理分析来确定GLCM分析的方法是角第二矩,对比度,不同矩反比,熵及其相关性。该识别系统使用GLCM方法使用10张叶片测试图像,并且堪培拉距离的准确度最高,为92.00%。使用20和30个测试数据得出的识别率分别为50.67%和60.00%。

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