Discrete Spectral Hashing for Efficient Similarity Retrieval

• Published in: IEEE transactions on image processing Vol. 28; no. 3; pp. 1080 - 1091
• Main Authors: Hu, Di, Nie, Feiping, Li, Xuelong
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Binary codes; Complexity; Data analysis; Demand analysis; discrete spectral hashing; Eigenvalues and eigenfunctions; Image management; Image retrieval; Laplace equations; Manifolds; Matrix decomposition; Quantization (signal); Rotation; Spectral analysis; Spectral rotation; State of the art
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2018.2875312

To meet the required huge data analysis, organization, and storage demand, the hashing technique has got a lot of attention as it aims to learn an efficient binary representation from the original high-dimensional data. In this paper, we focus on the unsupervised spectral hashing due to its effective manifold embedding. Existing spectral hashing methods mainly suffer from two problems, i.e., the inefficient spectral candidate and intractable binary constraint for spectral analysis. To overcome these two problems, we propose to employ spectral rotation to seek a better spectral solution and adopt the alternating projection algorithm to settle the complex code constraints, which are therefore named as Spectral Hashing with Spectral Rotation and Alternating Discrete Spectral Hashing, respectively. To enjoy the merits of both methods, the spectral rotation technique is finally combined with the original spectral objective, which aims to simultaneously learn better spectral solution and more efficient discrete codes and is called as Discrete Spectral Hashing. Furthermore, the efficient optimization algorithms are also provided, which just take comparable time complexity to existing hashing methods. To evaluate the proposed three methods, extensive comparison experiments and studies are conducted on four large-scale data sets for the image retrieval task, and the noticeable performance beats several state-of-the-art spectral hashing methods on different evaluation metrics.