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Painting Analysis Using Wavelets and Probabilistic Topic Models

2014-01-26 · Tong Wu, Gungor Polatkan, David Steel, William Brown, Ingrid Daubechies, Robert Calderbank

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.

📄 PDF Abstract BibTeX arXiv:1401.6638

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ClusteringTopic Models

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