Earticle

초록

영어

This paper reports the results of experiments in improving performance of leaf identification system using Principal Component Analysis (PCA). The system involved combination of features derived from shape, vein, color, and texture of leaf. PCA was incorporated to the identification system to convert the features into orthogonal features and then the results were inputted to the classifier that used Probabilistic Neural Network (PNN). This approach has been tested on two datasets, Foliage and Flavia, that contain various color leaves (foliage plants) and green leaves respectively. The results showed that PCA can increase the accuracy of the leaf identification system on both datasets.

목차

Abstract
 1. Introduction
 2. Basic of the Proposed System
  2.1 Features of the System
  2.2 PNN for Leaf Identification
  2.3 Principal Component Analysis
 3. Model of the Proposed System
 4. Experimental Results
 5. Conclusions
 References

키워드

저자

• Abdul Kadir [ Gadjah Mada University, Indonesia ]
• Lukito Edi Nugroho [ Gadjah Mada University, Indonesia ]
• Adhi Susanto [ Gadjah Mada University, Indonesia ]
• Paulus Insap Santosa [ Gadjah Mada University, Indonesia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Science and Technology

• 간기

  월간

• pISSN

  2005-4238

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605