What Makes Paris Look Like Paris? - Archive ouverte HAL Access content directly
Journal Articles Communications of the ACM Year : 2015

What Makes Paris Look Like Paris?

(1) , (1) , (1) , (2, 3) , (4)
1
2
3
4

Abstract

Given a large repository of geo-tagged imagery, we seek to automatically find visual elements, for example 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.
Fichier principal
Vignette du fichier
Doersch15author.pdf (4.04 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01248528 , version 1 (31-12-2015)

Identifiers

Cite

Carl Doersch, Saurabh Singh, Abhinav Gupta, Josef Sivic, Alexei Efros. What Makes Paris Look Like Paris?. Communications of the ACM, 2015, 58 (12), pp.103-110. ⟨10.1145/2830541⟩. ⟨hal-01248528⟩
144 View
537 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More