Issue No. 12 - December (2009 vol. 31)
Brian Kulis , University of California at Berkeley, Berkeley
Prateek Jain , University of Texas at Austin, Austin
Kristen Grauman , University of Texas at Austin, Austin
We introduce a method that enables scalable similarity search for learned metrics. Given pairwise similarity and dissimilarity constraints between some examples, we learn a Mahalanobis distance function that captures the examples' underlying relationships well. To allow sublinear time similarity search under the learned metric, we show how to encode the learned metric parameterization into randomized locality-sensitive hash functions. We further formulate an indirect solution that enables metric learning and hashing for vector spaces whose high dimensionality makes it infeasible to learn an explicit transformation over the feature dimensions. We demonstrate the approach applied to a variety of image data sets, as well as a systems data set. The learned metrics improve accuracy relative to commonly used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases.
Metric learning, similarity search, locality-sensitive hashing, LogDet divergence, kernel learning, image search.
B. Kulis, K. Grauman and P. Jain, "Fast Similarity Search for Learned Metrics," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 2143-2157, 2009.