Abstract: 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.