Electrical Engineering
      and Computer Sciences

Electrical Engineering and Computer Sciences

COLLEGE OF ENGINEERING

UC Berkeley

   

2010 Research Summary

Hashing Algorithms for Scalable Image Search

View Current Project Information

Brian Kulis1 and Trevor Darrell

Defense Advanced Research Projects Agency

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 to learn 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.

Figure 1
Figure 1: Indexing scheme for efficient image search

[1]
http://nips.cc/Conferences/2009/Program/event.php?ID=1881

1Postdoc, EECS