Automatic architectural style detection using one-class support vector machines and graph kernels: Fid-Bau Portal

Automatic architectural style detection using one-class support vector machines and graph kernels

Strobbe, Tiemen / wyffels, Francis / Verstraeten, Ruben / Meyer, Ronald De / Campenhout, Jan Van

Abstract In this paper, we address the problem of automatic detection of architectural designs belonging to a particular architectural style or corpus. A solution to this problem could be useful in a number of situations: the systematic and automatic (historical) analysis of large design corpora, to leverage computer-aided design tools that assist designers in predicting performance measures that are difficult or time-consuming to calculate, or as a complementary method to generative design models, both in the formulation and evaluation of these models. In particular, we propose the use of one-class support vector machines (SVMs) with graph kernels to learn architectural style from a single dataset of designs that belong to this particular style. As a result, the trained classifier can successfully detect new unobserved designs as similar or different from the learned style. Also, two experiments demonstrate the ability to learn an architectural style that can be sufficiently generalized to new designs.

Highlights • We examine how architectural style can be automatically detected from 2D floor plans. • Support vector machines and graph kernels are used to learn topological features of 2D floor plans. • The Malagueira housing project constitutes a stylistic coherent test set for the experiments. • An accuracy of approximately 85% is achieved after optimizing the support vector machine. • Experiments demonstrate the usefulness of the proposed method in automatic style analysis.