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Proceedings of Sixth International Congress on Information and Communication Technology pp 889–897Cite as

Generation and Extraction of Color Palettes with Adversarial Variational Auto-Encoders

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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 236))

Abstract

The process of creating a meaningful and perceptually pleasing color palette is an incredibly difficult task for the inexperienced practitioner. In this paper we show that the Variational Auto Encoder can be a powerful creative tool for the generation of novel color palettes as well as their extraction from visual mediums. Our proposed model is capable of extracting meaningful color palettes from images, and simultaneously learns an internal representation which allows for the sampling of novel color palettes without any additional input.

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Notes

  1. 1.

    https://colorpalettes.net/.

  2. 2.

    https://www.design-seeds.com/.

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Author information

Authors and Affiliations

  1. Graduate School of Fundamental Science and Engineering, Waseda University, Tokyo, Japan

    Ahmad Moussa & Hiroshi Watanabe

Authors
  1. Ahmad Moussa
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  2. Hiroshi Watanabe
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Corresponding author

Correspondence to Ahmad Moussa .

Editor information

Editors and Affiliations

  1. Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. JIS University, Kolkata, India

    Nilanjan Dey

  4. Global Knowledge Research Foundation, Ahmedabad, India

    Amit Joshi

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© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Cite this paper

Moussa, A., Watanabe, H. (2022). Generation and Extraction of Color Palettes with Adversarial Variational Auto-Encoders. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Proceedings of Sixth International Congress on Information and Communication Technology. Lecture Notes in Networks and Systems, vol 236. Springer, Singapore. https://doi.org/10.1007/978-981-16-2380-6_78

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