Recognizing landmarks using automated classification techniques: evaluation of various visual features

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.