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Folkioneer: Efficient Browsing of Community Geotagged Images on a Worldwide Scale

Mousselly-Sergieh, Hatem ; Watzinger, Daniel ; Huber, Bastian ; Döller, Mario ; Egyed-Zsigmond, Elöd ; Kosch, Harald (2014)
Folkioneer: Efficient Browsing of Community Geotagged Images on a Worldwide Scale.
Dublin, Ireland
doi: 10.1007/978-3-319-04117-9_36
Conference or Workshop Item, Bibliographie

Abstract

In this paper, we introduce Folkioneer, a novel approach for browsing and exploring community-contributed geotagged images. Initially, images are clustered based on the embedded geographical information by applying an enhanced version of the CURE algorithm, and characteristic geodesic shapes are derived using Delaunay triangulation. Next, images of each geographical cluster are analyzed and grouped according to visual similarity using SURF and restricted homography estimation. At the same time, LDA is used to extract representative topics from the provided tags. Finally, the extracted information is visualized in an intuitive and user-friendly manner with the help of an interactive map.

Item Type: Conference or Workshop Item
Erschienen: 2014
Creators: Mousselly-Sergieh, Hatem ; Watzinger, Daniel ; Huber, Bastian ; Döller, Mario ; Egyed-Zsigmond, Elöd ; Kosch, Harald
Type of entry: Bibliographie
Title: Folkioneer: Efficient Browsing of Community Geotagged Images on a Worldwide Scale
Language: English
Date: 2014
Publisher: Springer International Publishing
Book Title: MultiMedia Modeling - 20th Anniversary International Conference, MMM 2014, Dublin, Ireland, January 6-10, 2014, Proceedings, Part II
Event Location: Dublin, Ireland
DOI: 10.1007/978-3-319-04117-9_36
URL / URN: https://link.springer.com/chapter/10.1007%2F978-3-319-04117-...
Abstract:

In this paper, we introduce Folkioneer, a novel approach for browsing and exploring community-contributed geotagged images. Initially, images are clustered based on the embedded geographical information by applying an enhanced version of the CURE algorithm, and characteristic geodesic shapes are derived using Delaunay triangulation. Next, images of each geographical cluster are analyzed and grouped according to visual similarity using SURF and restricted homography estimation. At the same time, LDA is used to extract representative topics from the provided tags. Finally, the extracted information is visualized in an intuitive and user-friendly manner with the help of an interactive map.

Identification Number: TUD-CS-2014-1074
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
Date Deposited: 31 Dec 2016 14:29
Last Modified: 19 Sep 2018 14:18
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