Painting Analysis Using Wavelets and Probabilistic Topic Models

PAINTING ANALYSIS USING WAVELETS AND PROBABILISTIC TOPIC MODELS Tong Wu, Gungor Polatkan, David Steel, William Brown, Ingrid Daubechies and Robert Calderbank 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. Index Terms -- Painting Analysis, Wavelet Transforms, Hidden Markov Trees, Topic Models, Machine Learning 1. INTRODUCTION In recent years wavelet methods have contributed to art history through their application to forgery detection [1], linking of underdrawing and overpainting [2], and uncovering elements of style [3, 4].