Journal
KDD'18: PROCEEDINGS OF THE 24TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING
Volume -, Issue -, Pages 1485-1493Publisher
ASSOC COMPUTING MACHINERY
DOI: 10.1145/3219819.3219955
Keywords
supervised discrete hashing; robust M-estimator; rotation
Categories
Funding
- [NSFC-61572463]
- [NSF-Bigdata-1419210]
- [NSF-III-1360971]
- Direct For Computer & Info Scie & Enginr
- Div Of Information & Intelligent Systems [1360971] Funding Source: National Science Foundation
Ask authors/readers for more resources
Learning-based hashing has recently received considerable attentions due to its capability of supporting efficient storage and retrieval of high-dimensional data such as images, videos, and documents. In this paper, we propose a learning-based hashing algorithm called Robust Rotated Supervised Discrete Hashing ((RSDH)-S-2), by extending the previous work on Supervised Discrete Hashing (SDH). In (RSDH)-S-2, correntropy is adopted to replace the least square regression (LSR) model in SDH for achieving better robustness. Furthermore, considering the commonly used distance metrics such as cosine and Euclidean distance are invariant to rotational transformation, rotation is integrated into the original zero-one label matrix used in SDH, as additional freedom to promote flexibility without sacrificing accuracy. The rotation matrix is learned through an optimization procedure. Experimental results on three image datasets (MNIST, CIFAR-10, and NUS-WIDE) confirm that (RSDH)-S-2 generally outperforms SDH.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available