Local Feature based Image Similarity Functions for kNN Classification

In this paper we consider the problem of image content recognition and we address it by using local features and kNN based classification strategies. Specifically, we define a number of image similarity functions relying on local feature similarity and matching with and without geometric constrains. We compare their performance when used with a kNN classifier. Finally we compare everything with a new kNN based classification strategy that makes direct use of similarity between local features rather than similarity between entire images. As expected, the use of geometric information offers an improvement over the use of pure image similarity. However, surprisingly, the kNN classifier that use local feature similarity has a better performance than the others, even without the use of geometric information. We perform our experiments solving the task of recognizing landmarks in photos.