Abstract
In the study of computational aesthetics, we always use high-resolution paintings to analyze painting style, but actually the paintings we obtain mostly are low-resolution. In this paper, the contrast experiments based on sparse coding are carried out between high and low resolution paintings. Different features are extracted in frequency domain and Gabor domain from the basis function of sparse coding (SC). Then the normalized mutual information (NMI) is figured out to analyze the effect of different features for painting style. At last, the features with better performance are used to classify the paintings’ style. The results of experiments show that, to a certain extent, the features extracted from low-resolution paintings still have the ability to characterize the painting style, among which the Gabor energy has the best effect in the painting style analysis.
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Acknowledgment
It is a project supported by Natural Science Foundation of P.R. China (No. 61271361, 61263048, 61163019, 61462093), the Research Foundation of Yunnan Province (2014FA021, 2014FB113), and Digital Media Technology Key Laboratory of Universities in Yunnan.
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Zhu, J., Pu, Y.y., Xu, D., Qian, W.h., Wang, L.q. (2017). The Performance Analysis of Low-Resolution Paintings for Computational Aesthetics. In: Chen, Y., Christie, M., Tan, W. (eds) Smart Graphics. SG 2015. Lecture Notes in Computer Science(), vol 9317. Springer, Cham. https://doi.org/10.1007/978-3-319-53838-9_12
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DOI: https://doi.org/10.1007/978-3-319-53838-9_12
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