Treffer: Machine Learning-Based Classification of Historical Fashion Silhouettes Through a Digital Approach to Cultural Heritage.

The need to evaluate automated approaches arises not from a lack of expertise among historians but from the challenge of scaling, reproducing, and systematizing dress silhouette classification across large digital datasets. Automation is positioned here as a complement to expert knowledge, not a replacement. A dataset of 270 images from four periods—Empire, Romanticism, Victorian, and Art Nouveau—was processed with AI tools for background removal and standardization. Fifteen formal shape indices were calculated, selected through sequential evaluation, and classified using k-nearest neighbors, support vector machines, and decision trees. Initial analyses showed accuracy between 9.7% and 40.2%, but with the polynomial kernel in SVM, accuracy improved to 76–81%. Victorian dress silhouettes achieved the highest accuracy, while Empire dress silhouettes were the most difficult to classify. The study adds new empirical data and classification models to the literature, highlighting the methodological contribution of automated dress silhouette analysis to interdisciplinary heritage studies. Future work will expand datasets and incorporate adaptive algorithms, with potential applications in education, digital reconstruction, and fashion design. [ABSTRACT FROM AUTHOR]