International Journal of Application or Innovation in Engineering & Management
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ISSN 2319 – 4847
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Call for Paper, Published Articles, Indexing Infromation Content Based Image Retrieval Using Clustering , Authors : Ms. Urvashi Chavan, Prof. N. M. Shahane, International Journal of Application or Innovation in Engineering & Management (IJAIEM), www.ijaiem.org
Volume & Issue no: Volume 3, Issue 10, October 2014

Title:
Content Based Image Retrieval Using Clustering
Author Name:
Ms. Urvashi Chavan, Prof. N. M. Shahane
Abstract:
ABSTRACT Content-based image retrieval (CBIR) is a widely accepted technique for searching images from large and unlabeled image databases. But users are not satisfied with the traditional information retrieval techniques, because the network and development of multimedia technologies are becoming more popular. So the content based image retrieval (CBIR) are becoming a source of accurate and efficient retrieval. In recent years, various techniques have been implemented to improve the performance of CBIR. Clustering deals with searching a structure in a corpus of unlabeled data. Clustering is an unsupervised method of classification but it is observed that whenever we provide a small amount of supervision to clustering, it improves the clustering performance significantly. In this paper, different clustering techniques are discussed and analysed. Also, we propose a new CBIR system that uses kernel mean shift clustering technique under semi-supervised framework. This method uses only pairwise constraints to train the clustering procedure. This is a non-parametric method, so we are not restricted to specific no of clusters. Keywords: Content Based Image Retrieval(CBIR), Image retrieval, Semi-supervised clustering, Mean shift clustering, Feature extraction.
Cite this article:
Ms. Urvashi Chavan, Prof. N. M. Shahane , " Content Based Image Retrieval Using Clustering " , International Journal of Application or Innovation in Engineering & Management (IJAIEM), Volume 3, Issue 10, October 2014 , pp. 181-184 , ISSN 2319 - 4847.
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