Multiple feature kernel hashing for large-scale visual search
作者：
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.
摘要
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.
论文关键词：Locality-sensitive hashing,Multiple features,Compact hashing,Multiple kernels
论文评审过程：Received 26 December 2012, Revised 1 July 2013, Accepted 27 August 2013, Available online 6 September 2013.
论文官网地址：https://doi.org/10.1016/j.patcog.2013.08.022
