Earticle

초록

영어

Though clustering Analysis has been developed for many years with many clustering methods come into application, image clustering is still a difficult problem. One of the most fundamental problems is that there are many kinds of image representations, and the distinguish ability of each feature is different, so their cluster effects are also different. To decide cluster priority level of different images features on a specific image dataset, the distinguish ability of three typical image features are analyzed, and a cluster discriminant index is present, which called Simplified Overall Cluster Quality is composed of cluster compaction and cluster separation. Experimental results showed the feature with best distinguish ability also possessed best discriminant index. So this index can be used to decide the priority of features for clustering images or the best feature for image cluster.

목차

Abstract
 1. Introduction
 2. The Distinguish Ability of the Image Features
  2.1 The Feature Extraction
  2.2 The Distinguish Ability of Features
 3. Cluster Validation and the Overall Cluster Quality
  3.1 Cluster Validity
  3.2 Renyi Entropy and Overall Cluster Quality (OCQ)
  3.3 Simplified Overall Cluster Quality (SOCQ)
 4. Experiment
 5. Conclusion
 Acknowledgement 
 References

키워드

저자

• Tianqiang Peng [ Department of Computer Science and Engineering, Henan Institute of Engineering, Zhengzhou ]
• Haolin Gao [ Department of Data Engineering, Zhenzhou Information Science and Technology Institute, Zhengzhou, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605