Earticle

초록

영어

Nowadays the content based image retrieval (CBIR) is becoming a source of exact and fast retrieval. CBIR presents challenges in indexing, accessing of image data and how end systems are evaluated. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. Many clustering and segmentation algorithms both suffer from the limitation of the number of clusters specified by a human user. It is often impractical to expect a human with sufficient domain knowledge to be available to select the number of clusters (NC) to return. This paper discusses the image retrieval based on NC which is evaluated using hierarchical agglomerative clustering algorithm (HAC). In this paper, we determine the optimal number of clusters using HAC applied on RGB images and validate them using some validity indices. Based on number of clusters, we retrieve set of images. These cluster values can be further used for divide and conquer technology and indexing for large image dataset. An experimental study is presented on real data sets.

목차

Abstract
 1. Introduction
  1.1 Cluster validation
  1.2 Which Clustering Method Is the Best?
 2. An Experimental Study and Discussion
 3. Conclusion
 References

키워드

저자

• Monika Jain [ Research scholar, Department of computer science, Mewar university, Rajasthan, India ]
• Dr. S.K.Singh [ Professor and Head of Department of Information Technology, HRIT Engineering college, Ghaziabad, 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