Unsupervised Classification of Street Architectures Based on InfoGAN
This work addresses the need for cost-effective classification in urban planning by providing an unsupervised method, though it appears incremental as it adapts an existing technique to a specific domain.
The authors tackled the problem of classifying street architectures without labeled data by proposing an unsupervised framework based on InfoGAN, achieving practical and accurate results on a dataset of street view images from Nanjing, China.
Street architectures play an essential role in city image and streetscape analysing. However, existing approaches are all supervised which require costly labeled data. To solve this, we propose a street architectural unsupervised classification framework based on Information maximizing Generative Adversarial Nets (InfoGAN), in which we utilize the auxiliary distribution $Q$ of InfoGAN as an unsupervised classifier. Experiments on database of true street view images in Nanjing, China validate the practicality and accuracy of our framework. Furthermore, we draw a series of heuristic conclusions from the intrinsic information hidden in true images. These conclusions will assist planners to know the architectural categories better.