Earticle

초록

영어

Flower classification is a useful way for grouping a flower in certain class using specific features. This research propose a new method of flower classification system using combination of color and texture features. The first phase is getting the crown of the flower, which is localized from a flower image by using pillbox filtering and OTSU’s thresholding. In the next phase, color and texture features are extracted from the crown. The color features are extracted by removing L channel in L*a*b* color space, and taking only a* and b* channel, because of ignoring different lighting condition in flower image. The texture features are extracted by Segmentation-based Fractal Texture Analysis (SFTA). The combination features which are consisted of 10 color features and 48 texture features are used as input in k-Nearest Neighbor (kNN) classifier method with cosine distance. The flower classification achieves the best result with accuracy 73.63%.

목차

Abstract
 1. Introduction
 2. Research Method
  2.1 Preprocessing
  2.2. Feature Extraction
  2.3. Flower Classification
 3. Results and Analysis
  3.1. Perfomance Measure
  3.2. Results
 4. Conclusion and Future Work
 References

키워드

저자

• Yuita Arum Sari [ Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS) Surabaya, Indonesia ]
• Nanik Suciati [ Department of Informatics, Faculty of Information Technology, Institut Teknologi Sepuluh Nopember (ITS) Surabaya, Indonesia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605