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arXiv · 1905.12844

Unsupervised Classification of Street Architectures Based on InfoGAN

Ning Wang·Xianhan Zeng·Renjie Xie·Zefei Gao·Yi Zheng·Ziran Liao·Junyan Yang·Qiao Wang
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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.

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Ning Wang, Xianhan Zeng, Renjie Xie, Zefei Gao, Yi Zheng, Ziran Liao, Junyan Yang, Qiao Wang. 2019-05-30. Unsupervised Classification of Street Architectures Based on InfoGAN. https://arxiv.org/abs/1905.12844

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