2021/02/01 by Jorge Miguel Silva, Diogo Pratas, Rui Antunes +2 · 15 citations
Computer Science · Mathematics · Neuroscience · #Aesthetic Perception and Analysis #Art #Artificial intelligence #Authentication (law) #Block (permutation group theory) #Computer science #Data mining #Generative Adversarial Networks and Image Synthesis #Image Retrieval and Classification Techniques #Information retrieval #Mathematics #Measure (data warehouse) #Object (grammar) #Painting #Point (geometry) #Style (visual arts) #Visual arts #cs.CV #cs.IT #cs.LG #math.IT
paper · pdf · doi:10.1016/j.patcog.2021.107864
published in Pattern Recognition 114, 107864 (Elsevier BV) · Website: http://panther.web.ua.pt 24 Pages; 19 pages article; 5 pages supplementary material
openalex publication_date 2021/02/01 · arxiv created 2021/02/02 · arxiv updated 2021/02/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.