MACHINE VISION AND ITS IMPACT ON ART CRITICISM - Tewatia, Nidhi; Sarkar, Sudeshna; Sahastrabuddhae, Sanika; B. S. Seshadri; Tripathy, Alakananda; Diwate, Kajal Sanjay (ShodhKosh: Journal of Visual and Performing Arts, 2025)
Machine vision has become a revolution in the art criticism of the present time whereby computational tools have been presented to extend, challenge, and redefine long-standing conventions of aesthetic criticism or assessment. The fact that artworks are becoming multimodal and digitized and even hybrid in nature has increased what the algorithms can detect, encode style, and analyze the compositional structures, which has widened the scope of interpretive inquiry. This paper explores the research of using machine vision enabled by neural networks of convolutional convolution, transformers, and vision-language models to model visual information systematically, including texture, color harmony, spatial depth, symbolic and narrative hints. The paper puts AI-driven criticism into a wider framework of seeking the meaning and the role of viewers and machines by applying cognitive theories of perception as well as philosophical approaches to perception and authorship. The methodology consists of the carefully selected collections of paintings, sculptures and digital works of art, as well as the powerful preprocessing pipelines to extract features and perform multimodal embedding. Results show that machine vision improves already existing systems of critique by the quantification of aesthetic qualities, the discovery of latent stylistic similarities, and the multi-layered patterns of interpretation not always evident to human viewers. In addition, AI methods are impacting curatorial practice, providing insights in the form of data to plan an exhibition, research the provenance, and engage the audience. The argument also points out the negative and positive aspects of computational aesthetics, noting the necessity of both human-AI interpretive ecosystems which allow cultural sensitivity but are friendly to people.