Painting Analysis Using Wavelets and Probabilistic Topic Models
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.
Cite
@article{arxiv.1401.6638,
title = {Painting Analysis Using Wavelets and Probabilistic Topic Models},
author = {Tong Wu and Gungor Polatkan and David Steel and William Brown and Ingrid Daubechies and Robert Calderbank},
journal= {arXiv preprint arXiv:1401.6638},
year = {2014}
}
Comments
5 pages, 4 figures, ICIP 2013