Image landmark recognition with hierarchical K-means tree
| dc.contributor.author | Rischka, Magdalena | |
| dc.contributor.author | Conrad, Stefan | |
| dc.contributor.editor | Seidl, Thomas | |
| dc.contributor.editor | Ritter, Norbert | |
| dc.contributor.editor | Schöning, Harald | |
| dc.contributor.editor | Sattler, Kai-Uwe | |
| dc.contributor.editor | Härder, Theo | |
| dc.contributor.editor | Friedrich, Steffen | |
| dc.contributor.editor | Wingerath, Wolfram | |
| dc.date.accessioned | 2017-06-30T11:40:46Z | |
| dc.date.available | 2017-06-30T11:40:46Z | |
| dc.date.issued | 2015 | |
| dc.description.abstract | Today's giant-sized image databases require content-based techniques to handle the exploration of image content on a large scale. A special part of image content retrieval is the domain of landmark recognition in images as it constitutes a basis for a lot of interesting applications on web images, personal image collections and mobile devices. We build an automatic landmark recognition system for images using the Bag-of-Words model in combination with the Hierarchical K-Means index structure. Our experiments on a test set of landmark and non-landmark images with a recognition engine supporting 900 landmarks show that large visual dictionaries of size about 1M achieve the best recognition results. | en |
| dc.identifier.isbn | 978-3-88579-635-0 | |
| dc.identifier.pissn | 1617-5468 | |
| dc.language.iso | en | |
| dc.publisher | Gesellschaft für Informatik e.V. | |
| dc.relation.ispartof | Datenbanksysteme für Business, Technologie und Web (BTW 2015) | |
| dc.relation.ispartofseries | Lecture Notes in Informatics (LNI) - Proceedings, Volume P-241 | |
| dc.title | Image landmark recognition with hierarchical K-means tree | en |
| dc.type | Text/Conference Paper | |
| gi.citation.endPage | 464 | |
| gi.citation.publisherPlace | Bonn | |
| gi.citation.startPage | 455 | |
| gi.conference.date | 2.-3. März 2015 | |
| gi.conference.location | Hamburg |
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