Image Landmark Recognition with Hierarchical K-Means Tree

Today’s giant-sized image databases require content-based techniques to handle the exploration of image content on a large scale. A special part of image content retrieval is the domain of landmark recognition in images as it constitutes a basis for a lot of interesting applications on web images, personal image collections and mobile devices. We build an automatic landmark recognition system for images using the Bag-of-Words model in combination with the Hierarchical K-Means index structure. Our experiments on a test set of landmark and non-landmark images with a recognition engine supporting 900 landmarks show that large visual dictionaries of size about 1M achieve the best recognition results.