摘要
无论对于农业信息化还是对于生态保护,研究植物识别都是非常必要的。基于植物叶片的植物识别方法一直是植物学中的一个重要研究方向。植物叶片的颜色、形状、纹理等特征都可以用来作为识别依据（杜吉祥,2005;纪寿文等,2002;王晓峰等,
Due to the large difference between the same-class leaf images, many classical recognition methods do not satisfy the actual requirements of the plant leaf image recognition system. Based on maximum variance unfolding（MVU） and maximum variance projection（MVP） , a supervised orthogonal MVU algorithm was presented and was applied to plant leaf image recognition. By the algorithm, the high-dimensionality data were mapped to an optimal low-dimensionality subspace where the different-class samples were located further away, while the same-class samples were located closer. The local geometry structure of the low dimension manifold of the original high dimensionality data was preserved. The experimental results on real plant leaf databases showed that the proposed method was effective and feasible for plant leaf recognition.
出处
《林业科学》
EI
CAS
CSCD
北大核心
2013年第6期184-188,共5页
Scientia Silvae Sinicae
基金
国家自然科学基金项目(61272333)
关键词
流形学习
植物叶片识别
最大差异伸展
监督正交最大差异伸展
manifold learning
plant leaf recognition
maximum variance unfolding（MVU）
supervisedorthogonal MVU
