Computer Graphics and Geometric Modelling: Implementation & Algorithms
Computer Graphics and Geometric Modelling: Implementation & Algorithms
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Computer analysis of Van Gogh's complementary colours
Pattern Recognition Letters
Machine Vision and Applications
Studying digital imagery of ancient paintings by mixtures of stochastic models
IEEE Transactions on Image Processing
Graph-based methods for the automatic annotation and retrieval of art prints
Proceedings of the 1st ACM International Conference on Multimedia Retrieval
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This paper examines whether machine learning and image analysis tools can be used to assist art experts in the authentication of unknown or disputed paintings. Recent work on this topic has presented some promising initial results. Our reexamination of some of these recently successful experiments shows that variations in image clarity in the experimental datasets were correlated with authenticity, and may have acted as a confounding factor, artificially improving the results. To determine the extent of this factor's influence on previous results, we provide a new "ground truth" data set in which originals and copies are known and image acquisition conditions are uniform. Multiple previously-successful methods are found ineffective on this new confounding-factor-free dataset, but we demonstrate that supervised machine learning on features derived from Hidden-Markov-Tree-modeling of the paintings' wavelet coefficients has the potential to distinguish copies from originals in the new dataset.