Abstract
Inspired by the outstanding performance of sparse coding in applications of image denoising, restoration, classification, etc., we propose an adaptive sparse coding method for painting style analysis that is traditionally carried out by art connoisseurs and experts. Significantly improved over previous sparse coding methods, which heavily rely on the comparison of query paintings, our method is able to determine the authenticity of a single query painting based on estimated decision boundary. Firstly, discriminative patches containing the most representative characteristics of the given authentic samples are extracted via exploiting the statistical information of their representation on the DCT basis. Subsequently, the strategy of adaptive sparsity constraint which assigns higher sparsity weight to the patch with higher discriminative level is enforced to make the dictionary trained on such patches more exclusively adaptive to the authentic samples than via previous sparse coding algorithms. Relying on the learnt dictionary, the query painting can be authenticated if both better denoising performance and higher sparse representation are obtained, otherwise it should be denied. Extensive experiments on impressionist style paintings demonstrate efficiency and effectiveness of our method.
Zhi Gao and Mo Shan—denotes joint first author.
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Notes
- 1.
Due on one hand to the copyright issue, the high quality reproductions of the paintings in the museums are rarely publicly available even for research purpose, on the other hand to the fact that museums usually have no interests to acquire and keep paintings that are known as forgeries.
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Acknowledgement
This work is supported by these Grants: theory and methods of digital conservation for cultural heritage (2012CB725300), PSF Grant 1321202075, and the Singapore NRF under its IRC@SG Funding Initiative and administered by the IDMPO at the SeSaMe centre.
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Gao, Z., Shan, M., Cheong, LF., Li, Q. (2015). Adaptive Sparse Coding for Painting Style Analysis. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9004. Springer, Cham. https://doi.org/10.1007/978-3-319-16808-1_8
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