An image-clustering method based on cross-correlation of color histograms
Yifeng Wu, Kevin Hudson · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2005
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.