A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets

   International Journal of Managing Information Technology (IJMIT)

ISSN: 0975-5586 (Online); 0975-5926 (Print)

http://airccse.org/journal/ijmit/ijmit.html

Article:

A Survey On: Content Based Image Retrieval Systems Using Clustering Techniques For Large Data sets

Authors

Monika Jain1 and S. K. Singh2,

 1 Mewar university, India and 

2 HRIT Engineering college, India

Abstract

Content-based image retrieval (CBIR) is a new but widely adopted method for finding images from vast and unannotated image databases. As the network and development of multimedia technologies are becoming more popular, users are not satisfied with the traditional information retrieval techniques. So now a days the content based image retrieval (CBIR) are becoming a source of exact and fast retrieval. In recent years, a variety of techniques have been developed to improve the performance of CBIR. Data clustering is an unsupervised method for extraction hidden pattern from huge data sets. With large data sets, there is possibility of high dimensionality. Having both accuracy and efficiency for high dimensional data sets with enormous number of samples is a challenging arena. In this paper the clustering techniques are discussed and analyzed. Also, we propose a method HDK that uses more than one clustering technique to improve the performance of CBIR. This method makes use of hierarchical and divide and conquer K Means clustering technique with equivalency and compatible relation concepts to improve the performance of the K-Means for using in high dimensional datasets. It also introduced the feature like color, texture and shape for accurate and effective retrieval system.

Keywords

Content Based Image Retrieval , divide and conquer k-means, hierarchical 

Paper URL

http://airccse.org/journal/ijmit/papers/3411ijmit03.pdf

Volume URL

http://airccse.org/journal/ijmit/vol3.html