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Leaf Recognition Based on Binary Gabor Pattern and Extreme Learning Machine

  • Huisi Wu
  • Jingjing Liu
  • Ping Li
  • Zhenkun WenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9916)

Abstract

Automatic plant leaf recognition has been a hot research spot in the recent years, where encouraging improvements have been achieved in both recognition accuracy and speed. However, existing algorithms usually only extracted leaf features (such as shape or texture) or merely adopt traditional neural network algorithm to recognize leaf, which still showed limitation in recognition accuracy and speed especially when facing a large leaf database. In this paper, we present a novel method for leaf recognition by combining feature extraction and machine learning. To break the weakness exposed in the traditional algorithms, we applied binary Gabor pattern (BGP) and extreme learning machine (ELM) to recognize leaves. To accelerate the leaf recognition, we also extract BGP features from leaf images with an offline manner. Different from the traditional neural network like BP and SVM, our method based on the ELM only requires setting one parameter, and without additional fine-tuning during the leaf recognition. Our method is evaluated on several different databases with different scales. Comparisons with state-of-the-art methods were also conducted to evaluate the combination of BGP and ELM. Visual and statistical results have demonstrated its effectiveness.

Keywords

Leaf recognition Binary Gabor Pattern Extreme Learning Machine Leaf recognition processing batch 

Notes

Acknowledgments

This work was supported in part by grants from the National Natural Science Foundation of China (No. 61303101), the Shenzhen Research Foundation for Basic Research, China (Nos. JCYJ20150324140036846), the ShenzhenPeacock Plan (No. KQCX20130621101205783) and the Start-up Research Fund of Shenzhen University (Nos. 2013-827-000009).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.College of Computer Science and Software EngineeringShenzhen UniversityShenzhenChina
  2. 2.Department of Mathematics and Information TechnologyThe Hong Kong Institute of EducationHong KongChina

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