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- W3100435750 abstract "This paper addresses image classification through learning a compact and discriminative dictionary efficiently. Given a structured dictionary with each atom (columns in the dictionary matrix) related to some label, we propose crosslabel suppression constraint to enlarge the difference among representations for different classes. Meanwhile, we introduce group regularization to enforce representations to preserve label properties of original samples, meaning the representations for the same class are encouraged to be similar. Upon the crosslabel suppression, we donot resort to frequently-used ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>0</sub> -norm or ℓ <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sub> -norm for coding, and obtain computational efficiency without losing the discriminative power for categorization. Moreover, two simple classification schemes are also developed to take full advantage of the learnt dictionary. Extensive experiments on six data sets, including face recognition, object categorization, scene classification, texture recognition, and sport action categorization are conducted, and the results show that the proposed approach can outperform lots of recently presented dictionary algorithms on both recognition accuracy and computational efficiency." @default.
- W3100435750 created "2020-11-23" @default.
- W3100435750 creator A5011030324 @default.
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- W3100435750 date "2017-08-01" @default.
- W3100435750 modified "2023-10-16" @default.
- W3100435750 title "Cross-Label Suppression: A Discriminative and Fast Dictionary Learning With Group Regularization" @default.
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- W3100435750 doi "https://doi.org/10.1109/tip.2017.2703101" @default.
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