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Discovering Harmony: A Hierarchical Colour Harmony Model for Aesthetics Assessment

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Computer Vision -- ACCV 2014 (ACCV 2014)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9005))

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Abstract

Color harmony is an important factor for image aesthetics assessment. Although plenty of color harmony theories are proposed by artists and scientists, there is little firm consensus and ambiguous definition amongst them, or even contradictory between them, which causes the existing theories infeasible for image aesthetics assessment. In order to overcome the problem of conventional color harmony theories, in this paper, we propose a hierarchical unsupervised learning approach to learn the compatible color combinations from large dataset. By using this generative color harmony model, we attempt to uncover the underlying principles that generate pleasing color combinations based on natural images. The main advantage of our method is that no prior empirical knowledge of image aesthetics, color harmony or arts is needed to complete the task of color harmony assessment. The experimental results on the public dataset show that our method outperforms the conventional rule based image aesthetics assessment approach.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (Grant No. 61273365 and No. 61100120) and the Fundamental Research Funds for the Central Universities (No. 2013RC0304).

Author information

Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Peng Lu, Zhijie Kuang & Ruifan Li

  2. Raytheon BBN Technologies, Cambridge, MA, USA

    Xujun Peng

Authors
  1. Peng Lu
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  2. Zhijie Kuang
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  3. Xujun Peng
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  4. Ruifan Li
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Corresponding author

Correspondence to Peng Lu .

Editor information

Editors and Affiliations

  1. Technische Universität München, Garching, Bayern, Germany

    Daniel Cremers

  2. University of Adelaide, Adelaide, South Australia, Australia

    Ian Reid

  3. Keio University, Yokohama, Kanagawa, Japan

    Hideo Saito

  4. University of California at Merced, Merced, California, USA

    Ming-Hsuan Yang

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Lu, P., Kuang, Z., Peng, X., Li, R. (2015). Discovering Harmony: A Hierarchical Colour Harmony Model for Aesthetics Assessment. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9005. Springer, Cham. https://doi.org/10.1007/978-3-319-16811-1_30

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  • DOI: https://doi.org/10.1007/978-3-319-16811-1_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-16810-4

  • Online ISBN: 978-3-319-16811-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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