This chapter follows the attempts of computer vision researchers to teach computers ways of understanding artistic paintings and delves into algorithms for recognizing the subject in a painting and how a computer can get past the abstraction of art.
This work analyzes the semantic structure of abstract art through large-scale embedding visualization, uncovering how perceptual relationships organize artistic meaning, and establishes benchmark tasks for classification, cross-modal retrieval, and text-to-image generation to evaluate how AI models perceive and reprodu...
Hao-Wei Zhang, Yuanpei Zhao, Ji-Zhe Zhou et al.· 0 citations
This approach converts detailed visual features into descriptive terms, addressing a key challenge in art history, and connects the use of images as data with the semantic concerns of humanists, establishing vision-based computational art history as an area for future growth.
As an important category of visual art, oil painting's use and matching of colors directly determine the emotional expression and aesthetic value of the artwork. In scenarios such as digital preservation, art design assistance, and online exhibitions, how to efficiently and accurately extract representative colors from...
A novel approach to computationally decompose and reconstruct the painting process itself is introduced, revealing how artworks emerge through systematic region-aware brushstroke sequences that mirror a more natural artistic practice.
The research aims to determine the staged process of working on paintings by the landscape artist I. I. Shishkin (1832-1898). The article examines preparatory materials (drawings, sketches, studies) that guided the artist toward a completed artwork. New data are introduced confirming the systematic nature of the artist...
O. Tuminskaya· Pan-Art· 0 citations
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