Automatic analysis of artistic paintings using information-based measures

TL;DR

This study employs information-based complexity measures, specifically Normalized Compression and Block Decomposition Method, to analyze artistic paintings, revealing patterns that aid in authentication, classification, and understanding artistic styles.

Contribution

It introduces the use of NC and BDM for analyzing paintings, creating stylistic fingerprints, and improving classification methods with auxiliary features.

Findings

Both measures effectively classify paintings by style, author, and movement.

Combining NC with roughness enhances stylistic description.

Regional complexity and height difference correlation improve classification accuracy.

Abstract

The artistic community is increasingly relying on automatic computational analysis for authentication and classification of artistic paintings. In this paper, we identify hidden patterns and relationships present in artistic paintings by analysing their complexity, a measure that quantifies the sum of characteristics of an object. Specifically, we apply Normalized Compression (NC) and the Block Decomposition Method (BDM) to a dataset of 4,266 paintings from 91 authors and examine the potential of these information-based measures as descriptors of artistic paintings. Both measures consistently described the equivalent types of paintings, authors, and artistic movements. Moreover, combining the NC with a measure of the roughness of the paintings creates an efficient stylistic descriptor. Furthermore, by quantifying the local information of each painting, we define a fingerprint that describes critical information regarding the artists' style, their artistic influences, and shared techniques. More fundamentally, this information describes how each author typically composes and distributes the elements across the canvas and, therefore, how their work is perceived. Finally, we demonstrate that regional complexity and two-point height difference correlation function are useful auxiliary features that improve current methodologies in style and author classification of artistic paintings. The whole study is supported by an extensive website (http://panther.web.ua.pt) for fast author characterization and authentication.