Unsupervised Classification of Street Architectures Based on InfoGAN

2019/05/30 by Ning Wang, Wang, Ning, Xianhan Zeng +13
Computer Science · Engineering · #Automated Road and Building Extraction #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Music and Audio Processing #Video Surveillance and Tracking Methods

paper · pdf · doi:10.48550/arxiv.1905.12844

openalex publication_date 2019/05/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Auto-Encoding Variational Bayes
• Google Street View: Capturing the World at Street Level

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