Name
Abstract | ||
|---|---|---|
There are now billions of images stored on photo sharing websites. These images contain visual cues that reflect the geographical location of where the photograph was taken (e.g., New York City). Linking visual features in images to physical locations has many potential applications, such as tourism recommendation systems. However, the size and nature of these databases pose great challenges. For ... |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TBDATA.2016.2600564 | IEEE Transactions on Big Data |
Keywords | Field | DocType |
Clothing,Geology,Databases,Visualization,Big data,Support vector machines,Urban areas | Data mining,Computer science,Geolocation,Self-organizing map,Redundancy (engineering),Artificial intelligence,Cluster analysis,Recommender system,Visualization,Geotagging,Big data,Database,Machine learning | Journal |
Volume | Issue | Citations |
2 | 4 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dmitry Kit | 1 | 63 | 3.48 |
| Yu Kong | 2 | 412 | 24.72 |
| Yun Fu | 3 | 4267 | 208.09 |