Compressed representation of feature vectors using a Bloomier filter and its application to specific object recognition

Nearest neighbor search of feature vectors representing local features is often employed for specific object recognition. In such a method, it is required to store many feature vectors to match them by distance calculation. The number of feature vectors is, in general, so large that a huge amount of memory is needed for their storage. A way to solve this problem is to skip the distance calculation because no feature vectors need to be stored if there is no need to calculate the distance. In this paper, we propose a method of object recognition without distance calculation. The characteristic point of the proposed method is to use a Bloomier filter, which is far memory efficient than hash tables, for storage and matching of feature vectors. From experiments of planar and 3D specific object recognition, the proposed method is evaluated in comparison to a method with a hash table.