PUMA
Istituto di Scienza e Tecnologie dell'Informazione     
Amato G., Falchi F., Bolettieri P. Recognizing landmarks using automated classification techniques: evaluation of various visual features. In: MMEDIA 2010 - 2010 Second International Conferences on Advances in Multimedia (Athens/Glyfada, Greece, 13-19 June 2010). Proceedings, pp. 78 - 83. IEEE, 2010.
 
 
Abstract
(English)
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.
URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5501609
DOI: 10.1109/MMEDIA.2010.20
Subject Image indexing
Image classification
Recognition
Landmarks
H.3.1 Content Analysis and Indexing
H.3.3 Information Search and Retrieval


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