Painting Analysis Using Wavelets and Probabilistic Topic Models

TL;DR

This paper presents a novel method combining wavelet transforms and probabilistic topic models to analyze and distinguish painting styles, demonstrated on a 14th-century altarpiece.

Contribution

It introduces a new approach integrating wavelet features with hierarchical Bayesian models for stylistic analysis of paintings.

Findings

Unsupervised models effectively identify stylistic elements.

Method successfully discriminates between different painting styles.

Wavelet-based features enhance stylistic pattern recognition.

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.