Abstract
Various approaches to the construction of pattern recognition algorithms based on the representation of classes as convex hulls in a multidimensional feature space are considered. This trend is well suited for biometrics problems with a large number of classes and small volumes of learning samples by class, for example, for problems of recognizing people by faces or fingerprints. In addition to simple algorithms for a point hitting a convex hull, algorithms of the nearest convex hull with different approaches to assessing the proximity of a test point to the convex hull of classes are investigated. Comparative experimental results are given and the advantages and disadvantages of the proposed approach are formulated.
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Acknowledgments
The study is performed with partial support from the Russian Foundation for Basic Research (RFBR), grants 19-29-01009 and 18-29-02036.
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Nemirko, A. (2021). Image Recognition Algorithms Based on the Representation of Classes by Convex Hulls. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_4
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DOI: https://doi.org/10.1007/978-3-030-68821-9_4
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