Abstract By combining the class information, local information and reliability of the original data, a geodesic distance measure is given. Based on the distance measure, a robust supervised Isomap (RS-Isomap) is proposed and is applied to the plant leaf classification. Plant leaf image sets are firstly projected into the low-dimensional manifold subspace by RS-Isomap, and then the SVM classifier is applied to plant. Finally, the experiments are implemented on the 300 leaf images of 20 plant species. The experimental results show that the proposed method is effective and feasible.
ZHANG Shan-Wen,HUANG De-Shuang. A Robust Supervised Manifold Learning Algorithm and Its Application to Plant Leaf Classification[J]. , 2010, 23(6): 836-841.
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