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Visual and geographical data fusion to classify landmarks in geo-tagged images

Hughes, Mark, Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2012) Visual and geographical data fusion to classify landmarks in geo-tagged images. In: The 23rd Irish Conference on Artificial Intelligence and Cognitive Science, 17-19 Sept 2012, Dublin, Ireland. ISBN 978-3832532406

High level semantic image recognition and classification is a challenging task and currently is a very active research domain. Computers struggle with the high level task of identifying objects and scenes within digital images accurately in unconstrained environments. In this paper, we present experiments that aim to overcome the limitations of computer vision algorithms by combining them with novel contextual based features to describe geo-tagged imagery. We adopt a machine learning based algorithm with the aim of classifying classes of geographical landmarks within digital images. We use community contributed image sets downloaded from Flickr and provide a thorough investigation, the results of which are presented in an evaluation section.
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Uncontrolled Keywords:Landmark Classification; Visual-Spatial Data Fusion; Geo-Tagging
Subjects:Computer Science > Multimedia systems
Computer Science > Image processing
DCU Faculties and Centres:Research Institutes and Centres > Centre for Next Generation Localisation (CNGL)
Research Institutes and Centres > CLARITY: The Centre for Sensor Web Technologies
Published in: Proceedings of the 23rd Irish Conference on Artificial Intelligence and Cognitive Science (AICS 2012). . Logos Verlag Berlin . ISBN 978-3832532406
Publisher:Logos Verlag Berlin
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
Funders:European Framework Programme 7
ID Code:17924
Deposited On:18 Apr 2013 10:54 by Gareth Jones . Last Modified 22 Oct 2018 15:08

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