Forest hashing: Expediting large scale image retrieval

This paper introduces a hybrid method for searching large image datasets for approximate nearest neighbor items, specifically SIFT descriptors. The basic idea behind our method is to create a serial system that first partitions approximate nearest neighbors using multiple kd-trees before calling upon locally designed spectral hashing tables for retrieval. This combination gives us the local approximate nearest neighbor accuracy of kd-trees with the computational efficiency of hashing techniques. Experimental results show that our approach efficiently and accurately outperforms previous methods designed to achieve similar goals.