Architectural Style Classification Using Multinomial Latent Logistic Regression

Architectural style classification differs from standard classification tasks due to the rich inter-class relationships between different styles, such as re-interpretation, revival, and territoriality. In this paper, we adopt Deformable Part-based Models (DPM) to capture the morphological characteristics of basic architectural components and propose Multinomial Latent Logistic Regression (MLLR) that introduces the probabilistic analysis and tackles the multi-class problem in latent variable models. Due to the lack of publicly available datasets, we release a new large-scale architectural style dataset containing twenty-five classes. Experimentation on this dataset shows that MLLR in combination with standard global image features, obtains the best classification results. We also present interpretable probabilistic explanations for the results, such as the styles of individual buildings and a style relationship network, to illustrate inter-class relationships.