A novel locality-sensitive hashing for large scale image retrieva
We introduce a method that enables fast image search with efficient additive kernels and kernel localitysensitive hashing. Recent work has explored ways to generalize locality-sensitive hashing to accommodate arbitrary kernel functions which preserve the algorithm's sub-linear time, however existing methods still do not solve the problem of locality-sensitive in locality-sensitive hashing (LSH) algorithm and indirectly sacrifice the loss in accuracy of search results in order to allow fast queries. To improve the search accuracy, we show how to use explicit feature maps for the additive class of homogeneous kernels, which help for feature transformation and combine it with kernel locality-sensitive hashing. We validate our technique on large-scale datasets, and show that it improve the accuracy relative to commonly used methods and enables accurate and fast performance for example-based object classification, feature matching, and content-based retrieval.