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Color Space Exploration of Paintings Using a Novel Probabilistic Divergence

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

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Abstract

It is strange to think of a world without color. Color adds profound richness and spectacular variety to the phenomenon of visual experience. This paper explores the latent color space used by artists through various well-known paintings. We compare the paintings’ color spaces by introducing a novel probabilistic divergence to evaluate the separation (divergences) between two paintings in their color spaces. Our results show that there is a significant divergence in color spaces of the works created at different periods of history.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Texas State University, San Marcos, TX, USA

    Shounak Roychowdhury

Authors
  1. Shounak Roychowdhury
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Correspondence to Shounak Roychowdhury .

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Editors and Affiliations

  1. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Alberto Del Bimbo

  2. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Rita Cucchiara

  3. Department of Computer Science, Boston University, Boston, MA, USA

    Stan Sclaroff

  4. Dipartimento di Matematica e Informatica, University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Cloud & AI, JD.COM, Beijing, China

    Tao Mei

  6. Dipartimento di Ingegneria dell’Informazione, University of Firenze, Firenze, Italy

    Marco Bertini

  7. Computational Sciences Department, National Institute of Astrophysics, Optics and Electronics (INAOE), Tonantzintla, Puebla, Mexico

    Hugo Jair Escalante

  8. Dipartimento di Ingegneria “Enzo Ferrari”, Università di Modena e Reggio Emilia, Modena, Italy

    Roberto Vezzani

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Roychowdhury, S. (2021). Color Space Exploration of Paintings Using a Novel Probabilistic Divergence. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12663. Springer, Cham. https://doi.org/10.1007/978-3-030-68796-0_41

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  • DOI: https://doi.org/10.1007/978-3-030-68796-0_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68795-3

  • Online ISBN: 978-3-030-68796-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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