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Classification of Feature Set Using K-means Clustering from Histogram Refinement Method

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5 Author(s)

In this paper, we propose to use K-means clustering for the classification of feature set obtained from the histogram refinement method. Histogram refinement provides a set of features for proposed for Content Based Image Retrieval (CBIR). Standard histograms, because of their efficiency and insensitivity to small changes, are widely used for content based image retrieval. But the main disadvantage of histograms is that many images of different appearances can have similar histograms because histograms provide coarse characterization of an image. Hence histogram refinement method further refines the histogram by splitting the pixels in a given bucket into several classes based on color coherence vectors. Several features are calculated for each of the cluster and these features are further classified using the K-means clustering.

Published in:

Networked Computing and Advanced Information Management, 2008. NCM '08. Fourth International Conference on  (Volume:2 )

Date of Conference:

2-4 Sept. 2008