Journal
IEEE TRANSACTIONS ON MULTIMEDIA
Volume 19, Issue 3, Pages 571-585Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2016.2625747
Keywords
Cross-modal similarity search; cross-modal hashing (CMH); rank-order preserving
Categories
Funding
- National Natural Science Foundation of China [91646207, 61573352, 61672098, 91438105]
- Strategic Priority Research Program of the CAS [XDB02060009]
- Beijing Natural Science Foundation [4142057]
- Priority Academic Program Development of Jiangsu Higher Education Institutions
- Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology
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Due to the query effectiveness and efficiency, cross-modal similarity search based on hashing has acquired extensive attention in the multimedia community. Most existing methods do not explicitly employ the ranking information when learning hash functions, which is quite important for building practical retrieval systems. To solve this issue, this paper proposes a rank-order preserving hashing (RoPH) method with a novel regression-based rank-order preserving loss that has provable large margin property and is easy to optimize. Moreover, we jointly learn the binary codes and hash functions instead of using any relaxation trick. To solve the induced optimization problem, the alternating descent technique is adopted and each subproblem can be solved conveniently. Specifically, we show that the involved binary quadratic programming subproblem with respect to an introduced auxiliary binary variable satisfies submodularity, enabling us to use the off-the-shelf graph-cut algorithms to solve it exactly and efficiently. Extensive experiments on three benchmarks demonstrate that RoPH significantly improves the ranking quality over the state of the arts.
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