Hierarchical Spherical Hashing for Compressing High Dimensional Vectors

We present a hierarchical approach to compress large dimensional vectors using hyper spherical hashing functions. We provide a practical solution for learning hyper spherical hashing functions by partitioning the vectors and learning hyper spheres in subspaces. Our method is an efficient way to preserve the hashing properties of sub-space hashing functions to generate the full-hashing functions in a divide and conquer fashion. We demonstrate the performance of our approach on the ILSVRC2010 Validation dataset and two large scale datasets: ILSVRC2010 Train and Holidays+Flickr 1M with high dimensional representations of size 128000, 25600 and 12800 respectively. Our results highlight the compact nature of hyper spherical hashing functions which significantly outperform the state-of-the art methods at compression ratios of 512, 256 and 128. Furthermore, we boost the retrieval performance by introducing an as symetric distance for spherical hashing functions.