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Multi-modal Plant Leaf Recognition Based on Centroid-Contour Distance and Local Discriminant Canonical Correlation Analysis

  • Shanwen Zhang
  • Zhen WangEmail author
  • Yun Shi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10955)

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.

Keywords

Plant recognition Centroid-contour distance Local discriminant canonical correlation analysis (LDCCA) Feature extraction 

Notes

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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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Information EngineeringXijing UniversityXi’anChina

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