The Earth Mover's Distance as a Metric for Image Retrieval
International Journal of Computer Vision
Improving similarity measures of histograms using smoothing projections
Pattern Recognition Letters
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Signatures versus histograms: Definitions, distances and algorithms
Pattern Recognition
A fast distance between histograms
CIARP'05 Proceedings of the 10th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis and Applications
Image retrieval based on histogram of fractal parameters
IEEE Transactions on Multimedia
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The aim of this paper is to present a new method to compare modulo histograms. In these histograms, the type of elements are cyclic, for instance, the hue in colour images. The main advantage is that there is an important time-complexity reduction respect the methods presented before. The distance between histograms that we present is defined on a structure called signature, which is a lossless representation of histograms. We show that the computational cost of our distance is O($\mathcal{}z'$2), being $\mathcal{}z'$ the number of non-empty bins of the histograms. The computational cost of the algorithms presented in the literature depends on the number of bins of the histograms. In most of the applications, the obtained histograms are sparse, then considering only the non-empty bins makes the time consuming of the comparison drastically decrease. The distance and algorithms presented in this paper are experimentally validated on the comparison of images obtained from public databases.