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 short length 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 linear-based hashing method that focuses on both short and long-length hash codes for maintaining high precision.
Keywords
- Machine Learning: Classification
- Machine Learning Applications: Applications of Supervised Learning
- Multidisciplinary Topics and Applications: Information Retrieval
