Hashing Algorithms for Scalable Image Search

A common problem in large-scale data is that of quickly extracting nearest neighbors to a query from a large database. In computer vision, for example, this problem arises in content-based image retrieval, 3-D image reconstructions, human body pose estimation, object recognition problems, and other problems. This project focuses on developing algorithms for quickly and accurately performing large-scale image searches using hashing techniques. Some particular contributions include incorporating hashing methods for learned metrics as well as for performing locality-sensitive hashing over arbitrary kernel functions, two prominent scenarios arising in modern computer vision applications. Recent work has aimed at learning appropriate hash functions for a given image search task in order to minimize the memory overhead required for accurate searches. We have applied our algorithms to several large-scale data sets including the 80 million images of the Tiny Image data set and other large content-based image retrieval data sets. For more information about this project, contact Brian Kulis.