Viet Anh Nguyen
ABSTRACT
Binary hashing has been increasingly popular for efficient similarity search in large-scale vision problems. This paper presents a novel Supervised Discriminative Hashing (SDH) method by jointly modeling the global and local manifold structures. Specifically, a family of discriminative hash functions is designed to map data points of the original high-dimensional space into nearby compact binary codes while preserving the geometrical similarity and discriminant properties in both global and local neighborhoods. Furthermore, the quantization loss between the original data and the binary codes together with the even binary code distribution are also taken into account in the optimization to generate more efficient and compact binary codes. Experimental results have demonstrated the proposed method outperforms the state-of-the-art.
- A. Gionis et al. Similarity search in high dimensions via hashing. In Proc. VLDB, 1999. Google Scholar
Digital Library
- Y. Gong et al. Iterative quantization: a procrustean approach to learning binary codes for large-scale image retrieval. TPAMI, 2012. Google ScholarDigital Library
- X. He. Incremental semi-supervised subspace learning for image retrieval. In Proc. ACM MM, 2004. Google ScholarDigital Library
- B. Kulis and T. Darrell. Learning to hash with binary reconstructive embeddings. In Proc. NIPS, 2009.Google Scholar
- M. Norouzi and D. Fleet. Minimal loss hashing for compact binary codes. In Proc. Int. Conf. Mach. Learn., 2011.Google Scholar
- M. Raginsky and S. Lazebnik. Locality sensitive binary codes from shift-invariant kernels. In Proc. NIPS, 2009.Google Scholar
- C. Strecha et al. Ldahash: Improved matching with smaller descriptors. TPAMI, 2012. Google ScholarDigital Library
- J. Wang et al. Sequential projection learning for hashing with compact codes. In Proc. ICML, 2010.Google Scholar
- J. Wang et al. Semi-supervised hashing for large scale search. TPAMI, 2012. Google ScholarDigital Library
- Y. Weiss et al. Spectral hashing. In Proc. NIPS, 2008.Google ScholarDigital Library
- Z. Wen and Y. Yin. A feasible method for optimization with orthogonality constraints. Mathematical Programming, 2013.Google Scholar
- D. Zhang et al. Self-taught hashing for fast similarity search. In Proc. Annual ACM SIGIR Conf., 2010. Google ScholarDigital Library
Index Terms
- Supervised Discriminative Hashing for Compact Binary Codes
Recommendations
Tree-based compact hashing for approximate nearest neighbor search
Hashing methods map high-dimensional data onto compact binary codes for efficient retrieval. These methods focus on preserving the data similarity in Hamming distance between the mapped hash codes. In this paper we propose a novel hashing method ...
The ordinal relation preserving binary codes
Hashing algorithm has been widely used for efficient approximate nearest neighbor (ANN) search. Learning optimal hashing functions has been given focus and it is still a challenge. This paper aims to effectively and efficiently generate relative ...
Semi-Supervised Hashing for Large-Scale Search
Hashing-based approximate nearest neighbor (ANN) search in huge databases has become popular due to its computational and memory efficiency. The popular hashing methods, e.g., Locality Sensitive Hashing and Spectral Hashing, construct hash functions ...
Comments