Content-based image classification is a wide research field that addresses the landmark recognition problem. Among the many classification techniques proposed, the k-nearest neighbor (kNN) is one of the most simple and widely used methods. In this article, we use kNN classification and landmark recognition techniques to address the problem of monument recognition in images. We propose two novel approaches that exploit kNN classification technique in conjunction with local visual descriptors. The first approach is based on a relaxed definition of the local feature based image to image similarity and allows standard kNN classification to be efficiently executed with the support of access methods for similarity search. The second approach uses kNN classification to classify local features rather than images. An image is classified evaluating the consensus among the classification of its local features. In this case, access methods for similarity search can be used to make the classification approach efficient. The proposed strategies were extensively tested and compared against other state-of-the-art alternatives in a monument and cultural heritage landmark recognition setting. The results proved the superiority of our approaches. An additional relevant contribution of this work is the exhaustive comparison of various types of local features and image matching solutions for recognition of monuments and cultural heritage related landmarks.
Amato, G., Falchi, F., & Gennaro, C. (2015). Fast Image Classification for Monument Recognition. Journal on Computing and Cultural Heritage, 8(4), 1–25. https://doi.org/10.1145/2724727