1 research outputs found
Painting Analysis Using Wavelets and Probabilistic Topic Models
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.Comment: 5 pages, 4 figures, ICIP 201