Celebratio Mathematica — Daubechies

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T. Wu, G. Polatkan, D. Steel, W. Brown, I. Daubechies, and R. Calderbank: “Painting analysis using wavelets and probabilistic topic models,” pp. 3264–3268 in 2013 IEEE international conference on image processing (Melbourne, 15–18 September 2013). IEEE (Piscataway, NJ), 2013. ArXiv 1401.6638 incollection

In this paper, computer-based techniques for stylistic analysis of paintings are applied to the five panels of the 14th century Peruzzi Altarpiece by Giotto di Bondone. Features are extracted by combining a dual-tree complex wavelet transform with a hidden Markov tree (HMT) model. Hierarchical clustering is used to identify stylistic keywords in image patches, and keyword frequencies are calculated for sub-images that each contains many patches. A generative hierarchical Bayesian model learns stylistic patterns of keywords; these patterns are then used to characterize the styles of the sub-images; this in turn, permits to discriminate between paintings. Results suggest that such unsupervised probabilistic topic models can be useful to distill characteristic elements of style.

A. Robert Calderbank	Related
Tong Wu	Related
Güngör Polatkan	Related
David Steel	Related
William P. Brown	Related