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• W3082940886 abstract "Image set classification has drawn increasing attention and it has been widely applied to many real-life domains. Due to the existence of multiple images in a set, which contain various view appearance changes, image set classification is a rather challenging task. One potential solution is to learn powerful representations from multiple images to decrease the intra-class diversity and enlarge the inter-class separation. In this paper, we propose an optimal discriminative feature and dictionary learning (ODFDL) method, which attempts to learn a feature mapping matrix and a dictionary such that in the mapped feature space the inter-class sparse reconstruction error of data is maximized and the intra-class sparse reconstruction error is minimized. This learning strategy enforces the learned sparse representations from image sets have large inter-class separation and small intra-class scatter. Furthermore, to better exploit the non-linear information of data from different image sets, we also present two non-linear ODFDL methods, termed Kernel-ODFDL and Hierarchy-ODFDL to further improve the classification performance. Experiments on five commonly used image sets exhibit that our approaches are comparable with many state-of-the-arts." @default.
• W3082940886 created "2020-09-08" @default.
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• W3082940886 date "2021-02-01" @default.
• W3082940886 modified "2023-09-27" @default.
• W3082940886 title "Optimal discriminative feature and dictionary learning for image set classification" @default.
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• W3082940886 doi "https://doi.org/10.1016/j.ins.2020.08.066" @default.
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