In the past few years, computer vision and pattern recognition systems have been becoming increasingly more powerful, expanding the range of automatic tasks enabled by machine vision. Here we show that computer analysis of building images can perform quantitative analysis of architecture, and quantify similarities between city architectural styles in a quantitative fashion. Images of buildings from 18 cities and three countries were acquired using Google StreetView, and were used to train a machine vision system to automatically identify the location of the imaged building based on the image visual content. Experimental results show that the automatic computer analysis can automatically identify the geographical location of the StreetView image. More importantly, the algorithm was able to group the cities and countries and provide a phylogeny of the similarities between architectural styles as captured by StreetView images. These results demonstrate that computer vision and pattern recognition algorithms can perform the complex cognitive task of analyzing images of buildings, and can be used to measure and quantify visual similarities and differences between different styles of architectures. This experiment provides a new paradigm for studying architecture, based on a quantitative approach that can enhance the traditional manual observation and analysis. The source code used for the analysis is open and publicly available.
翻译:在过去几年里,计算机视觉和模式识别系统越来越强大,扩大了机器视觉所促成的自动任务范围。在这里,我们表明,建筑图像的计算机分析可以对建筑结构进行定量分析,并以定量方式量化城市建筑风格之间的相似性。18个城市和3个国家的建筑物图像是用Google StreetView获得的,并用来培训机器视觉系统,以便根据图像视觉内容自动识别图像建筑的位置。实验结果显示,自动计算机分析可以自动识别StreetView图像的地理位置。更重要的是,算法能够对城市和国家进行分组,并提供StreetView图像所捕捉到的建筑风格之间的相似性。这些结果显示,计算机视觉和模式识别算法可以履行分析建筑物图像的复杂认知任务,并可用于测量和量化不同结构风格之间的视觉相似性和差异。这一实验为研究结构提供了一个新的范式,其量化方法可以加强传统的手工观察和分析。用于分析的源代码是公开开放的。