Abstract
This paper proposes a combination of deep learning and hand-designed feature for plant identification based on leaf and flower images. The contributions of this paper are two-fold. First, for each organ image, we have performed a comparative evaluation of deep learning and hand-designed feature for plant identification. Two approaches for deep learning and hand-designed feature that are convolutional neuron network (CNN) and kernel descriptor (KDES) are chosen in our experiments. Second, based on the results of the first contribution, we propose a method for plant identification by late fusing the identification results of leaf and flower. Experimental results on ImageClef 2015 dataset show that hand designed feature outperforms deep learning for well-constrained cases (leaf captured on simple background). However, deep learning shows its robustness in natural situations. Moreover, the combination of leaf and flower images improves significantly the identification when comparing leaf-based plant identification.
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Acknowledgements
The authors thank Collaborative Research Program for Common Regional Issue (CRC) funded by ASEAN University Network (Aun-Seed/Net), under the grant reference HUST/CRC/1501.
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Thanh-Nhan Nguyen, T., Le, TL., Vu, H., Nguyen, HH., Hoang, VS. (2017). A Combination of Deep Learning and Hand-Designed Feature for Plant Identification Based on Leaf and Flower Images. In: Król, D., Nguyen, N., Shirai, K. (eds) Advanced Topics in Intelligent Information and Database Systems. ACIIDS 2017. Studies in Computational Intelligence, vol 710. Springer, Cham. https://doi.org/10.1007/978-3-319-56660-3_20
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DOI: https://doi.org/10.1007/978-3-319-56660-3_20
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