Multimedia Tools and Applications

, Volume 77, Issue 17, pp 22671–22688 | Cite as

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

  • Yu-Dong ZhangEmail author
  • Junding Sun


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.


Angiosperm genus Weight decay Classification Fractional fourier entropy Feature extraction Most abundant color index Color histogram Single-hidden layer feed-forward neural-network Petal image AlexNet 



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

Compliance with Ethical Standards

Conflicts of Interest

The authors declare no conflict of interest involved in this paper.


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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHenan Polytechnic UniversityJiaozuoPeople’s Republic of China
  2. 2.Translational Imaging Division & MRI UnitColumbia University and New York State Psychiatric InstituteNew YorkUSA
  3. 3.Department of InformaticsUniversity of LeicesterLeicesterUK

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