Paper Info
Title
VIRaL: Visual Image Retrieval and Localization
Abstract
New applications are emerging every day exploiting the huge data volume in community photo collections. Most focus on popular subsets, e.g., images containing landmarks or associated to Wikipedia articles. In this work we are concerned with the problem of accurately finding the location where a photo is taken without needing any metadata, that is, solely by its visual content. We also recognize landmarks where applicable, automatically linking them to Wikipedia. We show that the time is right for automating the geo-tagging process, and we show how this can work at large scale. In doing so, we do exploit redundancy of content in popular locations--but unlike most existing solutions, we do not restrict to landmarks. In other words, we can compactly represent the visual content of all thousands of images depicting e.g., the Parthenon and still retrieve any single, isolated, non-landmark image like a house or a graffiti on a wall. Starting from an existing, geo-tagged dataset, we cluster images into sets of different views of the same scene. This is a very efficient, scalable, and fully automated mining process. We then align all views in a set to one reference image and construct a 2D scene map. Our indexing scheme operates directly on scene maps. We evaluate our solution on a challenging one million urban image dataset and provide public access to our service through our online application, VIRaL.
Year
DOI
Venue
2011
10.1007/s11042-010-0651-7
Multimedia Tools Appl.
Keywords
DocType
Volume
Image retrieval,Image clustering,Sub-linear indexing,Geotagging,Location recognition,Landmark recognition,Image localization
Journal
51
Issue
ISSN
Citations 
2
1380-7501
44
PageRank 
References 
Authors
1.31
38
7
Name
Order
Citations
PageRank
Yannis Kalantidis186233.05
Giorgos Tolias272929.32
Yannis S. Avrithis3124076.86
Marios Phinikettos4441.31
Evaggelos Spyrou528731.50
Phivos Mylonas625244.52
Stefanos Kollias72268229.16