Abstract
Hashing can compress high-dimensional data into
compact binary codes, while preserving the similarity, to facilitate efficient retrieval and storage.
However, when retrieving using an extremely shortlength hash code learned by the existing methods,
the performance cannot be guaranteed because of
severe information loss. To address this issue, in this
study, we propose a novel supervised short-length
hashing (SSLH). In this proposed SSLH, mutual
reconstruction between the short-length hash codes
and original features are performed to reduce semantic loss. Furthermore, to enhance the robustness
and accuracy of the hash representation, a robust
estimator term is added to fully utilize the label information. Extensive experiments conducted on four
image benchmarks demonstrate the superior performance of the proposed SSLH with short-length hash
codes. In addition, the proposed SSLH outperforms
the existing methods, with long-length hash codes.
To the best of our knowledge, this is the first linearbased hashing method that focuses on both shortand long-length hash codes for maintaining high
precision