Multiple feature kernel hashing for large-scale visual search

Xianglong Liu, Junfeng He, Bo Lang

Abstract: Highlights•We propose a generic multiple feature hashing framework using multiple kernels.•Visual features are implicitly mapped and concatenated to reduce complexity.•We formulate both supervised and unsupervised hashing problems in the framework.•Alternating optimization ways efficiently learn hashing functions and the kernel space.•Experiments validate the superior performances and efficiency of the proposed approach.