基于隐式Naïve Bays分类器的叶片植物识别系统

Heba F. Eid, A. Hassanien, Tai-hoon Kim
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引用次数: 18

摘要

植物鉴定对植物物种管理至关重要。在不需要植物学家专业知识的情况下,需要一个自动植物识别系统来表征植物物种。本文提出了一种利用叶片数字图像进行植物物种识别的高效计算模型。该识别系统结合了叶片生物特征,利用叶片形状和脉状特征对叶片图像进行分类。提取10个组合的生物特征叶片特征,传递给Hidden naive bayes分类器进行分类。本文对黄花植物数据库中32种不同植物的1907个叶片样本进行了实验并进行了论证。其中,所提出的植物识别模型显示出97%的平均识别准确率。
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Leaf Plant Identification System Based on Hidden Naïve Bays Classifier
Plant identification is vital for the management of plant species. An automated plant identification system is required for the characterization of plant species without requiring the expertise of botanists. This paper presents an efficient and computational model for plant species identification using digital images of leaves. The proposed identification system combines the leaf biometric features, where shape and venation features are used for leaf image classification. 10 combined biometric leaf features are extracted and passed to Hidden naaive bays classifiers to be categorized. Several experiments are conducted and demonstrated on 1907 sample leaves of 32 different plant species taken form Flavia dataset. Where, the proposed plant identification model shows consistently performances of 97% average identification accuracy.
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