kNN based image classification relying on local feature similarity
Proceedings of the Third International Conference on SImilarity Search and APplications
Geometric consistency checks for kNN based image classification relying on local features
Proceedings of the Fourth International Conference on SImilarity Search and APplications
Proceedings of the ACM multimedia 2012 workshop on Geotagging and its applications in multimedia
Mobile capture of remote points of interest using line of sight modelling
Computers & Geosciences
Heterogeneous bag-of-features for object/scene recognition
Applied Soft Computing
International Journal of Reconfigurable Computing - Special issue on Selected Papers from the 2011 International Conference on Reconfigurable Computing and FPGAs (ReConFig 2011)
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In this paper, the performance of several visual features is evaluated in automatically recognizing landmarks (monuments, statues, buildings, etc.) in pictures. A number of landmarks were selected for the test. Pictures taken from a test set were classified automatically trying to guess which landmark they contained. We evaluated both global and local features. As expected, local features performed better given their capability of being less affected to visual variations and given that landmarks are mainly static objects that generally also maintain static local features. Between the local features, SIFT outperformed SURF and ColorSIFT.