Recognizing City Identity via Attribute Analysis of Geo-tagged Images
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Torralba_Recognizing city.pdf
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4.72 MB
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Author(s)
Zhou, Bolei
Liu, Liu
Oliva, Aude
Torralba, Antonio
Date Issued
2014
Journal
Computer Vision – ECCV 2014
Publisher
Springer-Verlag
Citation
Zhou, Bolei, Liu Liu, Aude Oliva, and Antonio Torralba. “Recognizing City Identity via Attribute Analysis of Geo-Tagged Images.” Lecture Notes in Computer Science (2014): 519–534.
Version
Author's final manuscript
Abstract
After hundreds of years of human settlement, each city has formed a distinct identity, distinguishing itself from other cities. In this work, we propose to characterize the identity of a city via an attribute analysis of 2 million geo-tagged images from 21 cities over 3 continents. First, we estimate the scene attributes of these images and use this representation to build a higher-level set of 7 city attributes, tailored to the form and function of cities. Then, we conduct the city identity recognition experiments on the geo-tagged images and identify images with salient city identity on each city attribute. Based on the misclassification rate of the city identity recognition, we analyze the visual similarity among different cities. Finally, we discuss the potential application of computer vision to urban planning.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Urban Studies and Planning
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Creative Commons Attribution-Noncommercial-Share Alike
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DOI of Published Version
https://doi.org/10.1007/978-3-319-10578-9_34
