Cross-Modal Discrete Hashing
作者：
Highlights：
• We present a new cross-modal discrete hashing (CMDH) approach to learn compact binary codes for cross-modal scalable multimedia search. We develop a discrete optimization framework to jointly learn binary codes and a series of hash functions for each modality, so that the performance drop due to the inferior optimization techniques can be avoided.
• We present two cross-modal hashing algorithms called CMDH-linear and CMDH-kernel under the proposed framework, which performs linear and non-linear mappings to learn binary codes.
• Experimental results on three benchmark datasets clearly show that our methods achieve competitive results with the state-of-the-arts in cross-modal hashing.
摘要
•We present a new cross-modal discrete hashing (CMDH) approach to learn compact binary codes for cross-modal scalable multimedia search. We develop a discrete optimization framework to jointly learn binary codes and a series of hash functions for each modality, so that the performance drop due to the inferior optimization techniques can be avoided.•We present two cross-modal hashing algorithms called CMDH-linear and CMDH-kernel under the proposed framework, which performs linear and non-linear mappings to learn binary codes.•Experimental results on three benchmark datasets clearly show that our methods achieve competitive results with the state-of-the-arts in cross-modal hashing.
论文关键词：Multimedia retrieval,Hashing,Cross-modal,Discrete optimization
论文评审过程：Received 11 August 2017, Revised 12 January 2018, Accepted 2 February 2018, Available online 6 February 2018, Version of Record 23 February 2018.
论文官网地址：https://doi.org/10.1016/j.patcog.2018.02.002
