Painting Analysis Using Wavelets and Probabilistic Topic Models
This work addresses the problem of automated stylistic discrimination in art analysis for art historians and conservators, but it is incremental as it applies existing methods to a specific dataset.
The paper tackled the problem of stylistic analysis of paintings by applying computer-based techniques to the Peruzzi Altarpiece, using wavelet transforms and probabilistic topic models to extract features and learn stylistic patterns, with results suggesting these models can distill characteristic elements of style.
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.