The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Fast Supervised Hashing with Decision Trees for High-Dimensional Data
Open accessAbstract
Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have demonstrated their advantage over linear ones due to their powerful generalization capability. In the literature, kernel functions are typically used to achieve non-linearity in hashing, which achieve encouraging retrieval perfor- mance at the price of slow evaluation and training time. Here we propose to use boosted decision trees for achieving non-linearity in hashing, which are fast to train and evaluate, hence more suitable for hashing with high dimensional data. In our approach, we first propose sub-modular formulations for the hashing binary code inference problem and an efficient GraphCut based block search method for solving large-scale inference. Then we learn hash func- tions by training boosted decision trees to fit the binary codes. Experiments demonstrate that our proposed method significantly outperforms most state-of-the-art methods in retrieval precision and training time. Especially for high- dimensional data, our method is orders of magnitude faster than many methods in terms of training time.
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Cites
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Matsui, Y., Ito, K., Aramaki, Y., Fujimoto, A., Ogawa, T., Yamasaki, T., & Aizawa, K. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-016-4020-z · Cited by 1,432
Shen, F., Shen, C., Liu, W., & Shen, H. T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298598 · Cited by 1,231
Self-citationSSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again
Kehl, W., Manhardt, F., Tombari, F., Ilic, S., & Navab, N. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.169 · Cited by 1,047
Semantics-preserving hashing for cross-view retrieval
Lin, Z., Ding, G., Hu, M., & Wang, J. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299011 · Cited by 542
Radisic, A. (2025) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.17117444 · Cited by 462
Metadata
- Authors
- Lin, G., Shen, C., Shi, Q., van den Hengel, A., & Suter, D.
- Venue
- 2014 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2014-06
- Pages
- 1971-1978
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2153273131
- Cited by
- 422
- References
- 26
- FWCI
- 28.65
- Type
- conference paper
- Language
- en
- Open access
- green
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- Lin, G., Shen, C., Shi, Q., van den Hengel, A., & Suter, D. (2014). Fast Supervised Hashing with Decision Trees for High-Dimensional Data. 2014 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2014.253
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