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Clustering of Multi-image Sets Using Rényi Information Entropy

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Bioinformatics and Biomedical Engineering (IWBBIO 2016)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 9656))

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Abstract

We propose a clustering method based on the calculation of variables derived from the \(\alpha \)-dependent Rényi information entropy – a point information gain entropy (\(H_\alpha \)) and point information gain entropy density (\(\varXi _\alpha \)), which measure an information-entropic distance between two multidimensional distributions. The matrices of \(H_\alpha \)/\(\varXi _\alpha \) values as functions of the parameter \(\alpha \) and a label of a multidimensional set’s object are classified into groups using a standard k-means algorithm. The method is presented on two multi-image series which in the origin, the number of images in the sets, the number of image color channels, and the pixel resolution differ.

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Acknowledgments

This work was financially supported by CENAKVA (No. CZ.1.05/2.1.00/01.0024), CENAKVA II (No.LO1205 under the NPU I program) and The CENAKVA Centre Development (No. CZ.1.05/2.1.00/19.0380).

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Authors and Affiliations

  1. Faculty of Fisheries and Protection of Waters, South Bohemian Research Center of Aquaculture and Biodiversity of Hydrocenoses, Institute of Complex Systems, University of South Bohemia in České Budějovice, Zámek 136, 373 33, Nové Hrady, Czech Republic

    Renata Rychtáriková

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  1. Renata Rychtáriková
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Correspondence to Renata Rychtáriková .

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Editors and Affiliations

  1. Universidad de Granada, Granada, Spain

    Francisco Ortuño

  2. Universidad de Granada, Granada, Spain

    Ignacio Rojas

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Rychtáriková, R. (2016). Clustering of Multi-image Sets Using Rényi Information Entropy. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_46

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  • DOI: https://doi.org/10.1007/978-3-319-31744-1_46

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-31743-4

  • Online ISBN: 978-3-319-31744-1

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