Abstract
The recently proposed \(l_2\)-norm based collaborative representation for classification (CRC) model has shown inspiring performance on face recognition after the success of its predecessor — the \(l_1\)-norm based sparse representation for classification (SRC) model. Though CRC is much faster than SRC as it has a closed-form solution, it may have the same weakness as SRC, i.e., relying on a “good” (properly controlled) training dataset for serving as its dictionary. Such a weakness limits the usage of CRC in real applications because the quality requirement is not easy to verify in practice. Inspired by the encouraging progress on dictionary learning for sparse representation, which can much alleviate this problem, we propose the discriminative collaborative representation (DCR) model. It has a novel classification model well fitting its discriminative learning model. As a result, DCR has the same advantage of being efficient as CRC, while at the same time showing even stronger discriminative power than existing dictionary learning methods. Extensive experiments on nine widely used benchmark datasets for both controlled and uncontrolled classification tasks demonstrate its consistent effectiveness and efficiency.
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Notes
- 1.
We follow LC-KSVD on balancing the two parts with a factor of 4 for simplicity, though a better factor may exist.
- 2.
Please refer to the supplementary material for more discussions and experimental results.
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Acknowledgement
This work was supported by “R&D Program for Implementation of Anti-Crime and Anti-Terrorism Technologies for a Safe and Secure Society”, Funds for integrated promotion of social system reform and research and development of the Ministry of Education, Culture, Sports, Science and Technology, the Japanese Government.
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Wu, Y., Li, W., Mukunoki, M., Minoh, M., Lao, S. (2015). Discriminative Collaborative Representation for Classification. In: Cremers, D., Reid, I., Saito, H., Yang, MH. (eds) Computer Vision -- ACCV 2014. ACCV 2014. Lecture Notes in Computer Science(), vol 9006. Springer, Cham. https://doi.org/10.1007/978-3-319-16817-3_14
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