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Modeling Characteristics of Location from User Photos
| Content Provider | ACM Digital Library |
|---|---|
| Author | Bakhshi, Saeideh Kennedy, Lyndon Kumar, Vikas Shamma, David A. |
| Abstract | In the past decade, location-based services have grown through geo-tagging and place-tagging. Proliferation of GPS-enabled mobile devices further enabled exponential growth in geo-tagged user content. On the other hand, location-based applications harness the abundance of geo-tagged content to further improve user experience and more relevant localized content. We show in this paper that geo-tagged content can vary significantly based on whether they are captured by a local versus a tourist to the location. Using photos shared by online users, we also show how we can learn unique characteristics about a given location. We finally discuss an effective metric to rank the most representative photos for a given location by combining visual contents and their social engagement potential. |
| Starting Page | 1 |
| Ending Page | 6 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781450349055 |
| DOI | 10.1145/3039677.3039683 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2017-03-13 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Location-aware Geo-tagged photos Content ranking Information theory |
| Content Type | Text |
| Resource Type | Article |
