The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Mapping the world's photos
Abstract
We investigate how to organize a large collection of geotagged photos, working with a dataset of about 35 million images collected from Flickr. Our approach combines content analysis based on text tags and image data with structural analysis based on geospatial data. We use the spatial distribution of where people take photos to define a relational structure between the photos that are taken at popular places. We then study the interplay between this structure and the content, using classification methods for predicting such locations from visual, textual and temporal features of the photos. We find that visual and temporal features improve the ability to estimate the location of a photo, compared to using just textual features. We illustrate using these techniques to organize a large photo collection, while also revealing various interesting properties about popular cities and landmarks at a global scale.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Introduction to information retrieval
(2009) · Choice Reviews Online · doi:10.5860/choice.46-2715 · Cited by 12,278
Cited by
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Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., & Li, L.-J. (2016) · Communications of the ACM · doi:10.1145/2812802 · Cited by 1,541
Measuring human perceptions of a large-scale urban region using machine learning
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2018.08.020 · Cited by 810
Uncovering social spammers: social honeypots + machine learning
Lee, K., Caverlee, J., & Webb, S. (2010) · Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval · doi:10.1145/1835449.1835522 · Cited by 619
Text-Based Twitter User Geolocation Prediction
Han, B., Cook, P., & Baldwin, T. (2014) · Journal of Artificial Intelligence Research · doi:10.1613/jair.4200 · Cited by 324
De Longueville, B., Smith, R. S., & Luraschi, G. (2009) · Proceedings of the 2009 International Workshop on Location Based Social Networks · doi:10.1145/1629890.1629907 · Cited by 307
Metadata
- Authors
- Crandall, D. J., Backstrom, L., Huttenlocher, D., & Kleinberg, J.
- Venue
- Proceedings of the 18th international conference on World wide web
- Publisher
- ACM
- Published
- 2009-04-20
- Pages
- 761-770
- OpenAlex
- W2103388840
- Cited by
- 769
- References
- 2
- FWCI
- 235.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Crandall, D. J., Backstrom, L., Huttenlocher, D., & Kleinberg, J. (2009). Mapping the world's photos. Proceedings of the 18th international conference on World wide web. https://doi.org/10.1145/1526709.1526812
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
