Quantization-based hashing: a general framework for scalable image and video retrieval.

•As far as we know, we are the first to propose a general framework to incorporate the quantization-based methods into the conventional similarity-preserving hashing, in order to improve the effectiveness of hashing methods. In theory, any quantization method can be adopted to reduce the quantization error of any similarity-preserving hashing methods to improve their performance.•This framework can be applied to both unsupervised and supervised hashing. We experimentally obtained the best performance compared to state-ofthe-art supervised and unsupervised hashing methods on six popular datasets.•We successfully show it to work on a huge dataset SIFT1B (1 billion data points) by utilizing the graph approximation and out-of-sample extension.