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Preliminary study on angiosperm genus classification by weight decay and combination of most abundant color index with fractional Fourier entropy

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Abstract

In order to develop an efficient angiosperm-genus classification system, we first collected petal image of Hibiscus, Orchis, and Prunus, by digital camera, and remove the backgrounds by region-growing method. Next, we proposed a novel feature-extraction method, which combined most abundant color index (MACI) and introduced the fractional Fourier entropy (FRFE). Third, we submitted the 41 features to a single-hidden layer feedforward neural-network (SLFN), with weight decay (WD) to avoid overfitting. The 10 × 10-fold cross validation showed our method achieved an overall accuracy of 98.92%. Without weight decay, the overall accuracy decreased to 95.50%. Our experiments validated that optimal decay factor is 0.1, and optimal number of hidden neurons is 15. This proposed method is excellent. It performs better than six state-of-the-art approaches and AlexNet. The weight decay helps to enhance generalization of our classifier.

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Acknowledgements

This paper was supported by Natural Science Foundation of Jiangsu Province (BK20150983), Natural Science Foundation of China (61602250).

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Author notes

  1. Yu-Dong Zhang and Junding Sun contributed equally to this work.

Authors and Affiliations

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, Henan, 454000, People’s Republic of China

    Yu-Dong Zhang & Junding Sun

  2. Translational Imaging Division & MRI Unit, Columbia University and New York State Psychiatric Institute, New York, NY, 10032, USA

    Yu-Dong Zhang

  3. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Yu-Dong Zhang

Authors
  1. Yu-Dong Zhang
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  2. Junding Sun
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Correspondence to Yu-Dong Zhang.

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Zhang, YD., Sun, J. Preliminary study on angiosperm genus classification by weight decay and combination of most abundant color index with fractional Fourier entropy. Multimed Tools Appl 77, 22671–22688 (2018). https://doi.org/10.1007/s11042-017-5146-3

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  • DOI: https://doi.org/10.1007/s11042-017-5146-3

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