Investigation of image models for landmark classification
Hughes, Mark and Jones, Gareth J.F. and O'Connor, Noel E. (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
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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.
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