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Semi-supervised orthogonal discriminant projection for plant leaf classification

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Abstract

Plant classification based on the leaf images is an important and tough task. For leaf classification problem, in this paper, a new weight measure is presented, and then a dimensional reduction algorithm, named semi-supervised orthogonal discriminant projection (SSODP), is proposed. SSODP makes full use of both the labeled and unlabeled data to construct the weight by incorporating the reliability information, the local neighborhood structure and the class information of the data. The experimental results on the two public plant leaf databases demonstrate that SSODP is more effective in terms of plant leaf classification rate.

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Acknowledgments

This work is supported by the grants of the National Science Foundation of China (61473237 & 61272333 & 61171170), the Anhui Provincial Natural Science Foundation (1308085QF99 & 1408085MF129), Shaanxi provincial education department Foundation (2013JK1145), and the higher education development special fund of Shaanxi Private (XJ13ZD01).

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Authors and Affiliations

  1. Department of Electronics and Information Engineering, Xijing University, Xi′an, China

    Shanwen Zhang

  2. Electronic Engineering Institute, Hefei, China

    Yingke Lei & Yihua Hu

  3. School of Computer Science and Information Engineering, Tianjin University of Science & Technology, Tianjin, China

    Chuanlei Zhang

Authors
  1. Shanwen Zhang
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  2. Yingke Lei
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  3. Chuanlei Zhang
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  4. Yihua Hu
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Correspondence to Yihua Hu.

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Zhang, S., Lei, Y., Zhang, C. et al. Semi-supervised orthogonal discriminant projection for plant leaf classification. Pattern Anal Applic 19, 953–961 (2016). https://doi.org/10.1007/s10044-015-0488-9

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  • DOI: https://doi.org/10.1007/s10044-015-0488-9

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