Abstract
Leaf based plant species recognition plays an important research, but it is a challenging work because of the complexity and diversity of plant leaves. A multi-modal plant leaf recognition method is proposed based on centroid-contour distance (CCD) and local discriminant canonical correlation analysis (LDCCA). First, the CCD feature vector is extracted from each leaf image. Second, the extracted feature vectors of any two within-class leaves are integrated by LDCCA. Final, K-nearest neighbor classifier is applied to plant recognition. The experiment results on a public dataset validated the effectiveness of the proposed method.
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Acknowledgments
This work is supported by the China National Natural Science Foundation under grant Nos. 61473237, key research and development projects (2017ZDXM-NY-088), Key project (2016GY-141) of Shaanxi Department of Science and Technology. The authors would like to thank all the editors and anonymous reviewers for their constructive advice.
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Zhang, S., Wang, Z., Shi, Y. (2018). Multi-modal Plant Leaf Recognition Based on Centroid-Contour Distance and Local Discriminant Canonical Correlation Analysis. In: Huang, DS., Jo, KH., Zhang, XL. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10955. Springer, Cham. https://doi.org/10.1007/978-3-319-95933-7_8
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DOI: https://doi.org/10.1007/978-3-319-95933-7_8
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