Computer vision, human senses, and language of art
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ORIGINAL ARTICLE
Computer vision, human senses, and language of art Lev Manovich1 Received: 1 June 2020 / Accepted: 14 October 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract What is the most important reason for using Computer Vision methods in humanities research? In this article, I argue that the use of numerical representation and data analysis methods offers a new language for describing cultural artifacts, experiences and dynamics. The human languages such as English or Russian that developed rather recently in human evolution are not good at capturing analog properties of human sensorial and cultural experiences. These limitations become particularly worrying if we want to compare thousands, millions or billions of artifacts—i.e. to study contemporary media and cultures at their new twenty-first century scale. When we instead use numerical measurements of image properties standard in Computer Vision, we can better capture details of a single artifact as well as visual differences between a number of artifacts–even if they are very small. The examples of visual dimensions that numbers can capture better then languages include color, shape, texture, contours, composition, and visual characteristics of represented faces, bodies and objects. The methods of finding structures and relationships in large numerical datasets developed in statistics and machine learning allow us to extend this analysis to very big datasets of cultural objects. Equally importantly, numerical image features used in Computer Vision also give us a new language to represent gradual and continuous temporal changes—something which natural languages are also bad at. This applies to both single artworks such as a film or a dance piece (describing movement and rhythm) and also to changes in visual characteristics in millions of artifacts over decades or centuries. Keywords Computer vision · Digital humanities · Cultural analytics · Language of art
1 Computer vision and digital humanities Researches in humanities research, write and argue about cultural images. They analyze and interpret content, visual style, author’s intentions, audience reception, meanings, emotional effects, and other aspects of images’ creation and circulation. Researchers in Computer Vision field also work with images, but their goals are very different—to teach computers to automatically understand images and enable automatic actions using visual information. The examples of these applications include their use in self-driving cars, industrial and home robots, medical diagnostics, contentbased image retrieval. What are the intellectual consequences of adopting Computer Vision methods in humanities research? What happens to humanists’ understanding of images and assumptions * Lev Manovich [email protected] 1
Program in Computer Science, The Graduate Center, City University of New York, New York, USA
about how to describe and study visual cultures in this meeting? How can we bring together assumptions and goals of AI research in gene
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