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Proceedings Paper

An image-clustering method based on cross-correlation of color histograms
Author(s): Yifeng Wu; Kevin Hudson
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Paper Abstract

Color histogram analysis is a powerful tool for characterizing color images. It has been widely used in image indexing and retrieval systems. A key problem to use color histogram in image classification is to find a robust similarity measurement between different color histograms. In this paper, we propose to use a cross-correlation function to measure color histogram similarity. We show that a cross-correlation function has several advantages over the method of histogram intersection, which has been widely used to calculate the similarity between color histograms: A cross-correlation function is normalized automatically; it can determine the similarity irrespective of image size; it is invariant to small color shift; it is easier to implement using the computationally efficient methods. We present an example of unsupervised image clustering by applying cross-correlation function to color histograms. This method was used to improve the perceived color consistency in a multi-print-engine system. We also show how to optimize the cross-correlation function to compensate for the color shift.

Paper Details

Date Published: 17 January 2005
PDF: 8 pages
Proc. SPIE 5682, Storage and Retrieval Methods and Applications for Multimedia 2005, (17 January 2005); doi: 10.1117/12.585569
Show Author Affiliations
Yifeng Wu, Hewlett-Packard Co. (United States)
Kevin Hudson, Hewlett-Packard Co. (United States)

Published in SPIE Proceedings Vol. 5682:
Storage and Retrieval Methods and Applications for Multimedia 2005
Rainer W. Lienhart; Noboru Babaguchi; Edward Y. Chang, Editor(s)

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