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  • Ký hiệu PL/XG: 006.6
    Nhan đề: Discriminative Dictionary Pair Learning For Image Classification /
DDC 006.6
Tác giả CN Nguyen, Hoang Vu
Nhan đề Discriminative Dictionary Pair Learning For Image Classification / Nguyen Hoang Vu, Tran Quoc Cuong, Tran Thanh Phong
Tóm tắt Dictionary learning (DL) for sparse coding has been widely applied in the field of computer vision. Many DL approaches have been developed recently to solve pattern classification problems and have achieved promising performance. In this paper, to improve the discriminability of the popular dictionary pair learning (DPL) algorithm, we propose a new method called discriminative dictionary pair learning (DDPL) for image classification. To achieve the goal of signal representation and discrimination, we impose the incoherence constraints on the synthesis dictionary and the low- rank regularization on the analysis dictionary. The DDPL method ensures that the learned dictionary has a powerful discriminative ability and signals are more separable after coding. We evaluate the proposed method on benchmark image databases in comparison with existing DL methods. The experimental results demonstrate that our method outperforms many recently proposed dictionary learning approaches.
Từ khóa tự do Dictionary learning
Từ khóa tự do Incoherent dictionary
Từ khóa tự do Synthesis and analysis dictionary
Từ khóa tự do Face recognition
Tác giả(bs) CN Tran, Quoc Cuong
Tác giả(bs) CN Tran, Thanh Phong
Nguồn trích Tạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics 2020tr. 53-69 Số: 04 Tập: 36
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082 |a006.6
10010|aNguyen, Hoang Vu
245 |aDiscriminative Dictionary Pair Learning For Image Classification / |cNguyen Hoang Vu, Tran Quoc Cuong, Tran Thanh Phong
520 |aDictionary learning (DL) for sparse coding has been widely applied in the field of computer vision. Many DL approaches have been developed recently to solve pattern classification problems and have achieved promising performance. In this paper, to improve the discriminability of the popular dictionary pair learning (DPL) algorithm, we propose a new method called discriminative dictionary pair learning (DDPL) for image classification. To achieve the goal of signal representation and discrimination, we impose the incoherence constraints on the synthesis dictionary and the low- rank regularization on the analysis dictionary. The DDPL method ensures that the learned dictionary has a powerful discriminative ability and signals are more separable after coding. We evaluate the proposed method on benchmark image databases in comparison with existing DL methods. The experimental results demonstrate that our method outperforms many recently proposed dictionary learning approaches.
653 |aDictionary learning
653 |aIncoherent dictionary
653 |aSynthesis and analysis dictionary
653 |aFace recognition
700 |aTran, Quoc Cuong
700 |aTran, Thanh Phong
7730 |tTạp chí Tin học và Điều khiển học = Journal of Computer Science And Cybernetics |d2020|gtr. 53-69|x1813-9663|v36|i04
890|a0|b0|c1|d1
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