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 de- scribed 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, open- ing 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. Copyright 2010 ACM.
Amato, G., & Falchi, F. (2010). kNN based image classification relying on local feature similarity. Proceedings of the Third International Conference on SImilarity Search and APplications - SISAP ’10, 101. https://doi.org/10.1145/1862344.1862360