Large-Scale Quantitative Analysis of Painting Arts

Abstract

Scientists have made efforts to understand the beauty of painting art in their own languages. As digital image acquisition of painting arts has made rapid progress, researchers have come to a point where it is possible to perform statistical analysis of a large-scale database of artistic paints to make a bridge between art and science. Using digital image processing techniques, we investigate three quantitative measures of images – the usage of individual colors, the variety of colors, and the roughness of the brightness. We found a difference in color usage between classical paintings and photographs, and a significantly low color variety of the medieval period. Interestingly, moreover, the increment of roughness exponent as painting techniques such as chiaroscuro and sfumato have advanced is consistent with historical circumstances.

Universality and diversity in human song

“…In natural language, for example, a few words appear with very high frequency, such as pronouns, while a great many are rare, such as the names of species of trees, but any sample will nevertheless tend to contain several rare words (111). A similar pattern is found in the distribution of colors among paintings in a given period of art history (112). In music, Zipf's law has been observed in We found that both the worldwide melodic and rhythmic bigram distributions followed power laws ( Fig.…”

Heterogeneity in chromatic distance in images and characterization of massive painting data set

“…A recent development that is proving to have far-reaching implications for a scientific exploration of human actions and behavior in many social, cultural complex systems is the increasing availability of massive high-quality data that allows a large-scale application of scientific frameworks and verifi-cation [2][3][4][5][6][7]. In the area of culture, subjects on which quantitative pattern-finding have been performed to a degree include literature [8][9][10][11][12] where Polish linguist Wincenty Lutosławski's work on the statistical features of word usage in Plato's Dialogue [8] is well known, music [13][14][15][16][17], and painting [18][19][20][21][22][23][24][25][26]. A landmark scientific study of paintings can be found in Taylor et al's characterisation of Jackson Pollock's ) drip paintings using fractal geometry to distinguish between authentic Pollocks and those of unknown origins [18], demonstrating that an artistic style can be quantified.…”

“…A landmark scientific study of paintings can be found in Taylor et al's characterisation of Jackson Pollock's ) drip paintings using fractal geometry to distinguish between authentic Pollocks and those of unknown origins [18], demonstrating that an artistic style can be quantified. More recent examples regarding painting include Lyu et al's wavelet-based decomposition of images [20], Hughes et al's sparse-coding models for authenticating artworks [21], Kim et al's characterization of variations in chiaroscuro technique via the so-called "roughness exponent" from statistical physics [22] and Gatys et al's style representation derived from correlations between the different features in different layers in a Convolutional Neural Network [23]. Besides quantification of artistic styles, some studied perceived similarities between different paintings [24], the influence relationships between artworks for quantifying creativity in an artwork [25], and the changes in the perception of beauty using face-recognition on images from different eras [26].…”

Psychological Interpretation of Drawings by Color Analysis for Mental Therapy

“…It is also helpful for a therapist with a short-term experience. The effectiveness of such quantitative analysis has been recently reported in many researches for artistic paintings [4].…”