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Leaf Recognition Based on DPCNN and BOW

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Abstract

Leaf classification is an interesting and important research. Current work focuses mainly on feature extraction, especially on textural feature extraction. In this case, we propose a new method of leaf recognition based on bag of words (BOW) and entropy sequence (EnS). In our method, EnS is firstly obtained by dual-output pulse-coupled neural network and then it is improved by BOW. Locality-constrained linear coding method is used for sparse coding. Then, the classification system is built where the linear support vector machine is taken as classifier. Some representative datasets and existing methods are employed to evaluate the effect of the proposed method. Finally, experimental results show that the accuracy of our proposed method is better than existing methods.

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Acknowledgements

This work was jointly supported by Fundamental Research Funds for the Central Universities (Grant No. lzujbky-2015-197), China Postdoctoral Science Foundation (Grant No. 2013M532097), National Science Foundation of China (Grant No. 61201421) and Science Foundation of Gansu Province of China (No.1208RJYA058).

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University Lanzhou, Lanzhou, 730000, Gansu Province, China

    Zhaobin Wang, Xiaoguang Sun, Zekun Yang & Yide Ma

  2. Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences Lanzhou, Lanzhou, 730000, Gansu Province, China

    Zhaobin Wang & Yaonan Zhang

  3. Institute of Biology, Gansu Academy of Sciences, Lanzhou, 730000, China

    Ying Zhu

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  1. Zhaobin Wang
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  2. Xiaoguang Sun
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Correspondence to Zhaobin Wang.

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Wang, Z., Sun, X., Yang, Z. et al. Leaf Recognition Based on DPCNN and BOW. Neural Process Lett 47, 99–115 (2018). https://doi.org/10.1007/s11063-017-9635-1

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