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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References
Bloomberg D (2008) Leptonica: color quantization using modified median cut. https://www.semanticscholar.org/paper/Color-quantization-using-modified-median-cut-Bloomberg-Leptonica/d246923c5d559445b4d699d6fe413895250156d3
Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling 1412:3555
Cho W, Bahng H, Park D, Yoo S, Wu Z, Ma X, Choo J (2018) Coloring with Words: Guiding Image Colorization Through Text-Based Palette Generation. ECCV 12:443–459
Cho J, Yun S, Lee K, Choi J (2017) PaletteNet: image recolorization with given color palette, 1058–1066. https://doi.org/10.1109/CVPRW.2017.143
Gervautz M, Purgathofer W (1988) A simple method for color quantization: octree quantization. In: Magnenat-Thalmann N, Thalmann D (eds) New trends in computer graphics. Springer, Berlin, Heidelberg, pp 219–231. 978-3-642-83492-9
Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2014) Generative adversarial nets. In: Ghahramani Z, Welling M, Cortes C, Lawrence ND, Weinberger KQ (eds) Advances in neural information processing systems, vol 27. Curran Associates, Inc., pp 2672–2680
Gowda SN, Yuan C(2019) ColorNet: investigating the importance of color spaces for image classification. arXiv:1902.00267
Heckbert P(1982) Color image quantization for frame buffer display. Assoc Comput Machinery SIGGRAPH Comput Graph 297–307. https://doi.org/10.1145/965145.801294
Heusel M, Ramsauer H, Unterthiner T, Nessler B, Hochreiter S (2018) GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash. Equilibrium 1706:08500
Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Kingma DP, Welling M (2014) Auto-encoding variational Bayes. In: 2nd International Conference on Learning Representations ICLR Proceedings. http://arxiv.org/abs/1312.6114
Larsen ABL, Sønderby SK, Larochelle H, Winther O (2016) Autoencoding beyond pixels using a learned similarity metric. In: Proceedings of the 33rd international conference on machine learning, in PMLR, vol 48, pp 1558–1566
MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the fifth berkeley symposium on mathematical statistics and probability, volume 1: Statistics. University of California Press, pp 281–297
Xudong M, Qing L, Haoran Xie, Lau RYK, Wang Z, Smolley SP (2017) Least squares generative adversarial networks. arXiv:1611.04076
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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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DOI: https://doi.org/10.1007/978-981-16-2380-6_78
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