Fast image search for learned metrics

We introduce a method that enables scalable image search for learned metrics. Given pairwise similarity and dissimilarity constraints between some images, we learn a Mahalanobis distance function that captures the images' underlying relationships well. To allow sub-linear 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 make it infeasible to learn an explicit weighting over the feature dimensions. We demonstrate the approach applied to a variety of image datasets. Our learned metrics improve accuracy relative to commonly-used metric baselines, while our hashing construction enables efficient indexing with learned distances and very large databases.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Prateek Jain, Brian Kulis, Kristen Grauman
• Keywords: Commonly-used Metric Baselines, Computer Vision, CVPR 2008, Enables Efficient Indexing, Metric Learning, Metric Parameterization, Time Similarity Search