“What Makes Paris Look Like Paris?” by Erwin Gianchandani.
From the post:
We all identify cities by certain attributes, such as building architecture, street signage, even the lamp posts and parking meters dotting the sidewalks. Now there’s a neat study by computer graphics researchers at Carnegie Mellon University — presented at SIGGRAPH 2012 earlier this month — that develops novel computational techniques to analyze imagery in Google Street View and identify what gives a city its character….
From the abstract:
Given a large repository of geotagged imagery, we seek to automatically find visual elements, e.g. windows, balconies, and street signs, that are most distinctive for a certain geo-spatial area, for example the city of Paris. This is a tremendously difficult task as the visual features distinguishing architectural elements of different places can be very subtle. In addition, we face a hard search problem: given all possible patches in all images, which of them are both frequently occurring and geographically informative? To address these issues, we propose to use a discriminative clustering approach able to take into account the weak geographic supervision. We show that geographically representative image elements can be discovered automatically from Google Street View imagery in a discriminative manner. We demonstrate that these elements are visually interpretable and perceptually geo-informative. The discovered visual elements can also support a variety of computational geography tasks, such as mapping architectural correspondences and influences within and across cities, finding representative elements at different geo-spatial scales, and geographically-informed image retrieval.
The video and other resources are worth the time to review/read.
What features do you rely on to “recognize” a city?
The potential to explore features within a city or between cities looks particularly promising.
