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Decision fusion for few-shot image classification

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Abstract

Recent few-shot learning methods mostly only use a single classifier to complete image classification. In general, a single classifier is likely to be overfitting because of its inherent drawbacks. However, the recognition rate of categorization will be significantly increased if we can utilize the complementary information of different classifiers. For the few-shot problem, the test samples come from new classes, which makes it difficult for a single classifier to distinguish, and it can be improved via decision fusion. In this paper, we propose decision fusion for few-shot learning (DF-FSL) to overcome the drawbacks of single classifier. To be specific, we assign the task to two classifiers, which are the logical regression classifier and probabilistic collaborative representation-based classifier (ProCRC), then allow the two classifiers to learn together through several iterations. Finally, we evaluate our approach on four benchmark image datasets, which include CIFAR-FS, CUB, miniImageNet and tieredImageNet datasets, and two remote sensing image datasets which are RSD46-WHU and NWPU-RESIS45. The experimental results illustrate the complementarity between different classifiers and show that the performance of our proposed DF-FSL method provides an obvious improvement. And DF-FSL can make great progress in few-shot remote sensing image classification.

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Acknowledgements

The paper was supported by the Natural Science Foundation of Shandong Province, China (Grant No. ZR2019MF073), the Fundamental Research Funds for the Central Universities, China University of Petroleum (East China) (Grant No. 20CX05001A), the Major Scientific and Technological Projects of CNPC (No. ZD2019-183-008).

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TY, WL, and BL contributed to the conception of the study, TY and BL performed the experiment; TY, WL, and BL contributed significantly to the analysis and manuscript preparation; TY and BL performed the data analyses and wrote the manuscript; WL and BL helped perform the analysis with constructive discussions. All authors reviewed the manuscript.

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Correspondence to Baodi Liu.

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Yuan, T., Liu, W., Yan, F. et al. Decision fusion for few-shot image classification. Int J Multimed Info Retr 12, 31 (2023). https://doi.org/10.1007/s13735-023-00281-w

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