Name
Abstract | ||
|---|---|---|
Large collaborative datasets offer the challenging opportunity of creating systems capable of extracting knowledge in the presence of noisy data. In this work we explore the ability to automatically learn tag semantics by mining a global geo-referenced image collection crawled from Flickr with the aim of improving an automatic annotation system. We are able to categorize sets of tags as places, landmarks, and visual descriptors. By organizing our dataset of more than 1.69 million images using a quadtree we can efficiently find geographic areas with sufficient density to provide useful results for place and landmark extraction. Precision-recall curves for our techniques compared with previous existing work used to identify place tags and manual groundtruth landmark annotation show the merit of our methods applied on a world scale. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1109/ICME.2009.5202776 | New York, NY |
Keywords | Field | DocType |
computer vision,data mining,geographic information systems,quadtrees,Flickr tag semantics,automatic annotation system,data mining,global annotation,global georeferenced image collection,groundtruth landmark annotation,large collaborative datasets,quadtree,Data Mining,Image Annotation,Knowledge Discovery | Geographic information system,Annotation,Automatic image annotation,Information retrieval,Visualization,Computer science,Knowledge extraction,Landmark,Semantics,Quadtree | Conference |
ISSN | ISBN | Citations |
1945-7871 E-ISBN : 978-1-4244-1291-1 | 978-1-4244-1291-1 | 22 |
PageRank | References | Authors |
1.26 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emily Moxley | 1 | 156 | 8.95 |
| Kleban, J. | 2 | 22 | 1.26 |
| Jiejun Xu | 3 | 147 | 11.11 |
| B. S. Manjunath | 4 | 7561 | 783.37 |