基于胡矩和局部二值模式的支持向量机树叶识别算法

Marko Lukic, Eva Tuba, M. Tuba
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引用次数: 39

摘要

叶片识别为植物分类提供了方便,是模式识别的一个重要分支。使用不同的叶子特征,如颜色、形状和纹理,以及不同的分类器,包括人工神经网络、k近邻和支持向量机。本文提出了一种基于调谐支持向量机作为分类器,以Hu矩和均匀局部二值模式直方图参数为特征的算法。我们提出的算法在标准基准数据库的叶子图像上进行了测试,并与文献中的其他方法进行了比较,结果证明该算法更成功(识别率更高)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leaf recognition algorithm using support vector machine with Hu moments and local binary patterns
Leaf recognition is convenient for plant classification and it is an important subfield of pattern recognition. Different leaf features such as color, shape and texture are used as well as different classifiers including artificial neural networks, k-nearest neighbor and support vector machines. In this paper we propose an algorithm based on tuned support vector machine as a classifier and Hu moments and uniform local binary pattern histogram parameters as features. Our proposed algorithm was tested on leaf images from standard benchmark database and compared with other approaches from literature where it proved to be more successful (higher recognition percentage).
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