基于GLCM、空隙度和Shen特征的植物识别系统模型
A Model of Plant Identification System Using GLCM, Lacunarity And Shen Features
摘要：
近年来，许多研究人员提出了许多方法来识别植物。目前，纹理、形状、颜色和纹理特征的应用已成为普遍的做法。然而，有许多可能的方法可以开发，以提高这种识别系统的性能。因此，本研究进行了多项实验。因此，一种新的方法，通过使用相结合的灰度共生矩阵，空隙度和沈特征和贝叶斯分类器给出了一个更好的结果相比，其他植物识别系统。为了比较，本研究使用了两种通常用于测试每个植物识别系统的性能的多个数据集。结果表明，该系统使用Flavia数据集时的准确率为97.19%，使用Foliage数据集时的准确率为95.00%，优于其他方法。
英文摘要：
Recently, many approaches have been introduced by several researchers to identify plants. Now, applications of texture, shape, color and vein features are common practices. However, there are many possibilities of methods can be developed to improve the performance of such identification systems. Therefore, several experiments had been conducted in this research. As a result, a new novel approach by using combination of Gray-Level Co-occurrence Matrix, lacunarity and Shen features and a Bayesian classifier gives a better result compared to other plant identification systems. For comparison, this research used two kinds of several datasets that were usually used for testing the performance of each plant identification system. The results show that the system gives an accuracy rate of 97.19% when using the Flavia dataset and 95.00% when using the Foliage dataset and outperforms other approaches.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
