Discrete Graph Hashing via Affine Transformation
Guohua Dong, Xiang Zhang, Long Lan, Xuhui Huang, Zhigang Luo · 2018
In unsupervised graph-based hashing for large-scale image retrieval, many efforts have been made to bridge the gap between the learned graph embedding and the corresponding binary codes. Relatively, few studies focus on the issue of the discrimination of graph embedding. In this paper, we firstly devise a discrete graph hashing model (DGH) that smooths graph embedding and simultaneously solving binary codes under the balanced discrete constraint, which equals a novel method of jointly learning graph embedding and spectral rotation, theoretically. To further induce discriminant graph embedding, we substitute affine transformation for spectral rotation in our DGH (abbreviated as ADGH). This is because affine transformation can accommodate both rotational angle and distance of graph embedding, while respecting the neighborhood structure among most samples. Besides, each subproblem of ADGH can yield the closed-form solution. Experiments of image retrieval on three benchmark datasets show that ADGH outperforms the representative hashing methods in quantity.