Earticle

초록

영어

Segmentation is a efficient technique of dividing the image into different regions or segments. Most of the researchers took clustering as the best method of segmenting an image. In clustering we try to increase the similarity within a same class and decrease the similarity between the classes. Many clustering algorithms were developed like FCM, FLICM and FELICM which are considered as the best algorithms to cluster the data. In our paper, we combine FELICM (Fuzzy Edge and Local Information C-Mean) with the negative selection algorithm. Negative selection algorithm is an evolutionary method which is based on artificial immune systems. The proposed method result shows us high accuracy results and even solves the problem of over segmentation.

목차

Abstract
 1. Introduction
 2. Proposed Methodology
 3. Experimental Results
 4. Performance Evaluation
 5. Conclusion
 References

키워드

저자

• Er. Pratibha Thakur [ Research Scholar Engineering DAV University, Jalandhar, Punjab, India ]
• Er. Sanjeev Dhiman [ Assistant Professor, Department of Computer Science and Engineering DAV University, Jalandhar, Punjab, 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