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arXiv · 1401.6638

Painting Analysis Using Wavelets and Probabilistic Topic Models

Tong Wu·Gungor Polatkan·David Steel·William Brown·Ingrid Daubechies·Robert Calderbank
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Abstract

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.

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Tong Wu, Gungor Polatkan, David Steel, William Brown, Ingrid Daubechies, Robert Calderbank. 2014-01-26. Painting Analysis Using Wavelets and Probabilistic Topic Models. https://arxiv.org/abs/1401.6638

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