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An investigation into colour combination in paintings via graph theory


 In visual arts, painting is deeply reliant on the colour combination for its impact, depth and emotion. Recently, many studies have focused on image processing, regarding identification and classification of images, using some colour features such as saturation, hue, luminance and so forth. This study aims to delve into some of the painting styles from the perspective of graph theory and network science. We compared a number of famous paintings to find out the likely pattern that an artist uses for colour combination and juxtaposition. To achieve this aim, the digital image of a painting is converted to a graph where each vertex represents one of the painting’s colours. In this graph, two vertices would be adjacent if and only if the two relative colours could be found in at least two adjacent pixels in the digital image. Among the several tools for network analysis, clustering, node centrality and degree distribution are used. Outcomes showed that artists unconsciously are following subtle mathematical rules to reach harmony and coordination in their work.