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).
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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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