Issue No. 06 - June (2012 vol. 34)
Brian Kulis , University of California at Berkeley, Berkeley
Kristen Grauman , University of Texas at Austin, Austin
Fast retrieval methods are critical for many large-scale and data-driven vision applications. Recent work has explored ways to embed high-dimensional features or complex distance functions into a low-dimensional Hamming space where items can be efficiently searched. However, existing methods do not apply for high-dimensional kernelized data when the underlying feature embedding for the kernel is unknown. We show how to generalize locality-sensitive hashing to accommodate arbitrary kernel functions, making it possible to preserve the algorithm's sublinear time similarity search guarantees for a wide class of useful similarity functions. Since a number of successful image-based kernels have unknown or incomputable embeddings, this is especially valuable for image retrieval tasks. We validate our technique on several data sets, and show that it enables accurate and fast performance for several vision problems, including example-based object classification, local feature matching, and content-based retrieval.
Similarity search, locality-sensitive hashing, central limit theorem, Kernel methods, image search.
Brian Kulis, Kristen Grauman, "Kernelized Locality-Sensitive Hashing", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1092-1104, June 2012, doi:10.1109/TPAMI.2011.219