2010, Contributo in atti di convegno, ENG
Amato G.; Falchi F.; Bolettieri P.
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.
2010, Contributo in atti di convegno, ENG
Amato G.; Falchi F.
In this paper, we propose a novel image classification approach, derived from the kNN classification strategy, that is particularly suited to be used when classifying images described by local features. Our proposal relies on the possibility of performing similarity search between image local features. With the use of local features generated over interest points, we revised the single label kNN classification approach to consider similarity between local features of the images in the training set rather than similarity between images, opening up new opportunities to investigate more efficient and effective strategies. We will see that classifying at the level of local features we can exploit global information contained in the training set, which cannot be used when classifying only at the level of entire images, as for instance the effect of local feature cleaning strategies. We perform several experiments by testing the proposed approach with different types of image local features in a touristic landmarks recognition task.