Computer Analysis of Architecture Using Automatic Image Understanding
This provides a new quantitative paradigm for studying architecture, enhancing traditional manual analysis for researchers and urban planners, though it is incremental in applying existing computer vision methods to a new domain.
The researchers tackled the problem of analyzing architectural styles from building images by developing a computer vision system that uses Google StreetView images from 18 cities across three countries to automatically identify geographical locations and quantify similarities between architectural styles, achieving successful grouping and phylogeny of cities and countries based on visual content.
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.