基于叶片图像和监督正交最大差异伸展的植物识别方法

doi:10.11707/j.1001-7488.20130627

Abstract

Abstract:

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.

Key words: manifold learning, plant leaf recognition, maximum variance unfolding(MVU), supervised orthogonal MVU

CLC Number:

TP391

Zhang Shanwen, Zhang Chuanlei, Wang Xuqi, Zhou Zhengguang, Zhang Yali. Plant Recognition Based on Leaf Image and Supervised Orthogonal Maximum Variance Unfolding[J]. Scientia Silvae Sinicae, 2013, 49(6): 184-188.

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Plant Recognition Based on Leaf Image and Supervised Orthogonal Maximum Variance Unfolding

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