Painting analysis using wavelets and probabilistic topic models

Published

Journal Article

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. © 2013 IEEE.

Full Text

Duke Authors

Cited Authors

  • Wu, T; Polatkan, G; Steel, D; Brown, W; Daubechies, I; Calderbank, R

Published Date

  • December 1, 2013

Published In

  • 2013 Ieee International Conference on Image Processing, Icip 2013 Proceedings

Start / End Page

  • 3264 - 3268

Digital Object Identifier (DOI)

  • 10.1109/ICIP.2013.6738672

Citation Source

  • Scopus