A Visual Content Analysis Framework for Quantifying Runway Silhouette Evolution: The Case of Stella McCartney’s Collections

Abstract

Fashion change in the twenty-first century has been shaped by technological, social, cultural, and aesthetic transformations, yet the long-term evolution of runway silhouettes is often discussed through descriptive interpretation rather than systematic measurement. This study addresses this gap by developing and applying a visual content analysis framework to Stella McCartney’s women’s Spring/Summer ready-to-wear collections from 2002 to 2025. A total of 1,014 runway looks were coded across four equal six-year periods according to five silhouette-related design dimensions: shoulder type, sleeve type, waistline position, fitting type, and overall silhouette. The reliability of the coding framework was tested, and the coded data were analysed to identify period-based and year-by-year changes. The findings show measurable but gradual transformation rather than abrupt design eras. Across the collections, regular and linear structures became less dominant, while relaxed, oversized, and volumetric configurations gained prominence. The study contributes a reproducible model for analysing fashion-image archives.