DATA-DRIVEN ANALYSIS OF VISUAL COMPOSITION PATTERNS - M V Madhusudhan; Srivastava, Richa; Sarpal, Sumeet Singh; Ali, Syed Sumera; Sinha, Shailendra Kumar; Gupta, Shikhar (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The paper provides an elaborate, data-driven approach to visual composition analysis of artistic, photographic, and design-based imagery. Conventional compositional ideals, including spatial balance, color harmony, Gestalt grouping, and narrative organization, are converted to quantifiable computational aspects using sophisticated image processing, perceptual modeling, as well as machine learning methods. With the incorporation of the feature-extraction, saliency-analysis, edge-orientation mapping, color-harmony measures, and object-position density modeling, the research findings indicate that the patterns of visual compositions are statistically significant and cross-cut across the genres and across the ages. Clustering and dimensionality reduction of patterns are additional ways of mining latent patterns, stylistic relationships, and trends of evolution hidden within large volumes of data. The framework has high interpretability based on the visual analytics using PCA-based feature embeddings, aggregated saliency heatmaps, line-orientation histograms, radar charts of chromatic attributes, and spatial distribution maps. Results indicate that composition is not intuitively or stylistically defined, but an empirically quantifiable structure, which can be used to improve creative pedagogy, computational aesthetics, and AI-mediated design systems. This piece of art defines a new analytical mode of approach, which crosses the boundary of artistic theory and machine intelligence, providing innovative conclusions on the role of compositional logic in the construction of perception and creative expression.