Abstract
Learning-based hashing has received increasing research attention due to its promising efficiency for large-scale similarity search. However, most existing manifold-based hashing methods cannot capture the intrinsic structure and discriminative information of image samples. In this paper, we propose a new learning-based hashing method, namely, Sparse Graph Hashing with Spectral Regression (SGHSR), for approximate nearest neighbor search. We first propose a sparse graph model to learn the real-valued codes which can not only preserves the manifold structure of the data, but also adaptively selects sparse and discriminative features. Then, we use a spectral regression to convert the real-valued codes into high-quality binary codes such that the information loss between the original space and the Hamming space can be well minimized. Extensive experimental results on three widely used image databases demonstrate that our SGHSR method outperforms the state-of-the-art unsupervised manifold-based hashing methods.
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Acknowledgement
This work was supported in part by the National Natural Science Foundation of China under Grants 62176066, 62106052 and 62006059, and in part by the Natural Science Foundation of Guangdong Province under Grant 2023A1515012717.
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He, Z., Qin, J., Fei, L., Zhao, S., Wen, J., Wang, B. (2024). Sparse Graph Hashing with Spectral Regression. In: Sheng, B., Bi, L., Kim, J., Magnenat-Thalmann, N., Thalmann, D. (eds) Advances in Computer Graphics. CGI 2023. Lecture Notes in Computer Science, vol 14498. Springer, Cham. https://doi.org/10.1007/978-3-031-50078-7_4
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