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Classification of sign-based image representations based on distance functions

  • Mathematical Methods in Pattern Recognition
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Abstract

The paper proposes various approaches to classifying sign-based representations of images based on distance functions. Any image is represented as a set of features describing differences in brightness. The construction of a distance function is proposed using classical functionals of information theory: the Shannon entropy and the Kullback-Leibler distance. It is shown that the Bayes classification in the case of independent features can be also described by distance functions. In the last section, the proposed approaches are evaluated using a face detection problem.

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Authors and Affiliations

  1. National Research University Higher School of Economics, Pokrovskii bul. 11, Moscow, 109028, Russia

    A. G. Bronevich

  2. CVisionLab, LLC, Severnaya Ploshchad’ 3, Taganrog, 347900, Russia

    A. V. Goncharov

Authors
  1. A. G. Bronevich
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  2. A. V. Goncharov
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Correspondence to A. G. Bronevich.

Additional information

Andrey G. Bronevich. Born 1966. Graduated from Taganrog State University of Radio Engineering 1988. Received candidate’s degree 1994 and doctoral degree 2004. Professor at National Research University Higher School of Economics. Scientific interests: nonadditive measures, theory of imprecise probabilities, decision theory under risk and uncertainty, pattern recognition, image processing. Member of Society for Imprecise Probability: Theories and Applications. Member of editorial board of International Journal of General Systems. Author of more than 90 papers.

Alexander V. Goncharov. Born 1984. Graduated from Taganrog State University of Radio Engineering 2006. Received candidate’s degree n 2010. Chief research and development officer at CVisionLab, LCC. Scientific interests: pattern recognition, machine learning, contentbased image retrieval.

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Bronevich, A.G., Goncharov, A.V. Classification of sign-based image representations based on distance functions. Pattern Recognit. Image Anal. 23, 175–183 (2013). https://doi.org/10.1134/S1054661813020053

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  • DOI: https://doi.org/10.1134/S1054661813020053

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