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In this paper we present a new method for high- dimensional descriptor matching, based on the KD-tree, which is a classic method for nearest neighbours search. This new method, which we name 3-way tree, avoids the boundary effects that disrupt the KD-tree in higher dimensionalities, by the addition of redundant, overlapping sub-trees. That way, more precision is obtained for the same querying times. We evaluate our method in the context of image identification for cultural collections, a task which can greatly benefit from the use of high-dimensional local descriptors computed around Pol (Points of Interest).

Matching Local Descriptors for Image Identification on Cultural Databases