Earticle

초록

영어

This paper presents three techniques of plants classification based on their leaf shape the SVM-BDT, PNN and Fourier moment technique for solving multiclass problems. All the three techniques have been applied to a database of 1600 leaf shapes from 32 different classes, where most of the classes have 50 leaf samples of similar kind. In the proposed work three techniques are used for comparing the performance of classification of leaves. Probabilistic Neural Network with principal component analysis, Support Vector Machine utilizing Binary Decision Tree and Fourier Moment. The proposed SVM based Binary Decision Tree architecture takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. This can lead to a dramatic improvement in recognition speed when addressing problems with large number of classes. Classification results from all the three techniques were compared and it was observed that SVM-BDT performs better than Fourier and PNN technique.

목차

Abstract
 1. Introduction
 2. Feature Extractions
  2.1. Basic Geometric Features
  2.2. Digital Morphological Features
 3. Classification Methodology
  3.1. Probabilistic Neural Networks
  3.2. Support vector machine
  3.3. Fourier moments
 4. Results and Conclusion
 References

키워드

저자

• Krishna Singh, [ Department of Electrical Roorkee Uttrakhand India ]
• Indra Gupta [ Department of Electrical Engineering IIT, Roorkee Uttrakhand India ]
• Sangeeta Gupta [ Forest Research Institute Dehradun Uttrakhand India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605