A team of researchers from the University of Girona and the Max Planck Institute in Germany has shown that some mathematical algorithms provide clues about the artistic style of a painting.

The composition of colours or certain aesthetic measurements can already be quantified by a computer, but machines are still far from being able to interpret art in the way that people do.

The researchers have shown that certain artificial vision algorithms mean a computer can be programmed to "understand" an image and differentiate between artistic styles based on low-level pictorial information. Human classification strategies, however, include medium and high-level concepts.

Low-level pictorial information encompasses aspects such as brush thickness, the type of material and the composition of the palette of colours. Medium-level information differentiates between certain objects and scenes appearing in a picture, as well as the type of painting (landscape, portrait, still life, etc.). High-level information takes into account the historical context and knowledge of the artists and artistic trends.

"It will never be possible to precisely determine mathematically an artistic period nor to measure the human response to a work of art, but we can look for trends," said Miquel Feixas, one of the authors of the study, published in the journal Computers and Graphics.

The researchers analysed various artificial vision algorithms used to classify art, and found that certain aesthetic measurements (calculating "the order" of the image based on analysing pixels and colour distribution), as well as the composition and diversity of the palette of colours, can be useful.

"This a painting of a seated woman with bent knee by Egon Schiele (1917). (Credit: Egon Schiele)"

Source: FECYT