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• W3025754292 abstract "With the development of deep learning techniques, fusion of deep features has demonstrated the powerful capability to improve recognition performance. However, most researchers directly fuse different deep feature vectors without considering the complementary and consistent information among them. In this paper, from the viewpoint of metric learning, we propose a novel deep feature fusion method, called deep feature fusion through adaptive discriminative metric learning (DFF-ADML), to explore the complementary and consistent information for scene recognition. Concretely, we formulate an adaptive discriminative metric learning problem, which not only fully exploits discriminative information from each deep feature vector, but also adaptively fuses complementary information from different deep feature vectors. Besides, we map different deep feature vectors of the same image into a common space by different linear transformations, such that the consistent information can be preserved as much as possible. Moreover, DFF-ADML is extended to a kernelized version. Extensive experiments on both natural scene and remote sensing scene datasets demonstrate the superiority and robustness of the proposed deep feature fusion method." @default.
• W3025754292 created "2020-05-21" @default.
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• W3025754292 date "2020-11-01" @default.
• W3025754292 modified "2023-09-28" @default.
• W3025754292 title "Deep feature fusion through adaptive discriminative metric learning for scene recognition" @default.
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• W3025754292 doi "https://doi.org/10.1016/j.inffus.2020.05.005" @default.
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