Efficient place recognition with canonical views

We study the problem of place recognition. Given a photo, we estimate its location by scene matching to a large database of internet photos of known locations. Traditional strategies, which involve a linear scan of the database to find matching scenes, fail to scale. On the other hand, internet photos contain a massive amount of noise and redundancy, which is of little help for place recognition. By exploiting the scene distribution of photos, we summarize the database by a set of canonical views. The set of canonical views eliminates the noise and redundancy in internet photos, and provides a compact representation for the database. By restricting scene matching to the set of canonical views, we observe a good tradeoff between efficiency and recall: the average processing time for a query photo is reduced by 97%, while the recall rate for place recognition remains at 75%.