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Investigation of image models for landmark classification

Hughes, Mark, Jones, Gareth J.F. orcid logoORCID: 0000-0003-2923-8365 and O'Connor, Noel E. orcid logoORCID: 0000-0002-4033-9135 (2009) Investigation of image models for landmark classification. In: the 4th International Workshop on Semantic Media Adaptation and Personalization (SMAP 2009),, December 2009, San Sebastián, Spain. ISBN 978-0-7695-3894-5

One commonly used approach to scene localisation and landmark recognition is to match an input image against a large annotated database of images using local image features. However a problem exists with these approaches with memory constraints and the processing time required to compare high dimensional image feature vectors in a very large scale database. In this paper we investigate a new landmark classification technique which takes advantage of the fact that there is considerable overlap in visually similar images of landmarks in any large public photo repository. A large number of images containing landmarks are clustered into visually similar clusters. Classification models are then implemented and trained based on global histograms of interest point features from these clusters to create models which can be used for robust real-time accurate classification of images containing these landmarks. We also investigate different techniques for the creation of these classification models to ascertain how best to guarantee a high level of robustness, accuracy and speed.
Item Type:Conference or Workshop Item (Paper)
Event Type:Workshop
Uncontrolled Keywords:Landmark recognition
Subjects:Computer Science > Information retrieval
DCU Faculties and Centres:Research Institutes and Centres > Centre for Digital Video Processing (CDVP)
DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Published in: Semantic Media Adaptation and Personalisation. . ISBN 978-0-7695-3894-5
Official URL:http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumb...
Copyright Information:© 2009 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:16134
Deposited On:03 May 2011 13:51 by Shane Harper . Last Modified 09 Nov 2018 10:12

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