IMAGE SIMILARITY SEARCH VIA HASHES WITH EXPANDED DIMENSIONALITY AND SPARSIFICATION

摘要

Image similarity searching can be achieved by improving utilization of computing resources so that computing power can be reduced while maintaining accuracy or accuracy can be improved using a same level of computing power. Such a similarity search can be achieved via an expansion matrix that expands the number of dimensions in an input feature vector of a query image. Dimensionality of an input feature vector can be increased, resulting in a higher dimensional hash. Sparsification can then be applied to the resulting higher dimensional hash. Sparsification can use a winner-take-all technique or setting a threshold, resulting in a hash of reduced length, but can still be considered of the expanded dimensionality. Matching the query image against a corpus of sample images can be achieved via nearest neighbor techniques via the resulting hashes to find sample images matching the query image.