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Plant identification via multipath sparse coding

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Abstract

In this paper, we propose a novel plant identification method based on multipath sparse coding using SIFT features, which avoids the need of feature engineering and the reliance on botanical taxonomy. In particular, the proposed method uses five paths to model the shape and texture features of plant images, and at each path it learns the dictionaries with different sizes using hierarchical sparse coding. Finally, we apply the learned representation for plant identification using linear SVM for classification. We evaluate the proposed method on several plant datasets and find that multi-organ is more informative than single organ for botanist. Experimental results also validate that the proposed method outperforms the state-of-the-art methods.

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  1. can be downloaded from http://www.vlfeat.org

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Acknowledgements

This work is supported in part by the national key R&D program of China (2015BAH52F03), the Natural Science Foundation of China (No. 61300111), Hubei Provincial Natural Science Foundation (NO.2014CFB659) and selfdetermined research funds of CCNU from the colleges’ basic research and operation of MOE (No.CCNU15A05023).

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Authors and Affiliations

  1. School of Information, Beijing Forestry University, Beijing, China

    Heyan Zhu

  2. School of Opto-Electronic Information, Yantai University, Yantai, China

    Heyan Zhu

  3. Institute of Animation and Digital Art, Communication University of China, Beijing, China

    Xinyuan Huang

  4. School of Computer Science and Technology, Harbin Institute of Technology, Weihai, China

    Shengping Zhang

  5. Department of Computer Science, Hong Kong Baptist University, Hong Kong, China

    Pong C. Yuen

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  1. Heyan Zhu
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  2. Xinyuan Huang
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  3. Shengping Zhang
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  4. Pong C. Yuen
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Correspondence to Xinyuan Huang.

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Zhu, H., Huang, X., Zhang, S. et al. Plant identification via multipath sparse coding. Multimed Tools Appl 76, 4599–4615 (2017). https://doi.org/10.1007/s11042-016-3538-4

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  • DOI: https://doi.org/10.1007/s11042-016-3538-4

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