Low-rank analysis–synthesis dictionary learning with adaptively ordinal locality

Li, Zhengming; Zhang, Zheng; Qin, Jie; Li, Sheng; Cai, Hongming

doi:10.1016/j.neunet.2019.07.013

“…Chen et al [4] proposed discriminative dictionary pair learning method based on differentiable support vector function (DPL-SV) for visual recognition. Li et al [15] proposed a discriminative low-rank analysis-synthesis dictionary learning (LR-ASDL) algorithm with the adaptively ordinal locality preserving (AOLP) term and low-rank model for object classification. To preserve the locality property of learned atoms in the synthesis dictionary, Zhang et al [35] proposed a locality constrained projective dictionary learning (LC-PDL) method.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Dictionary Pair Learning for Image Classification

Vu

¹

,

Cường

²

,

Phong

³

2020

JCC

1

0

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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 the powerful discriminative ability and the 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.

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“…Inspired by the superiority of 1,∞ norm [18], Wei et al [49] developed a fast DDL (FaDDL) method for synthetic aperture radar (SAR) image classification. The ordinal locality of analysis dictionary is not fully exploited in the above DPL and its variants, to tackle this problem, Li et al [50] proposed a discriminative low-rank analysissynthesis dictionary learning (LR-ASDL) algorithm with the adaptively ordinal locality.…”

Section: Introductionmentioning

confidence: 99%

Locality Constraint Dictionary Learning With Support Vector for Pattern Classification

Yin

¹

,

Wu

²

,

Chen

³

2019

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Discriminative dictionary learning (DDL) has recently gained significant attention due to its impressive performance in various pattern classification tasks. However, the locality of atoms is not fully explored in conventional DDL approaches which hampers their classification performance. In this paper, we propose a locality constraint dictionary learning with support vector discriminative term (LCDL-SV), in which the locality information is preserved by employing the graph Laplacian matrix of the learned dictionary. To jointly learn a classifier during the training phase, a support vector discriminative term is incorporated into the proposed objective function. Moreover, in the classification stage, the identity of test data is jointly determined by the regularized residual and the learned multi-class support vector machine. Finally, the resulting optimization problem is solved by utilizing the alternative strategy. Experimental results on benchmark databases demonstrate the superiority of our proposed method over previous dictionary learning approaches on both hand-crafted and deep features.

show abstract

“…Inspired by the superiority of 1,∞ norm [18], Wei et al [49] developed a fast DDL (FaDDL) method for synthetic aperture radar (SAR) image classification. The ordinal locality of analysis dictionary is not fully exploited in the above DPL and its variant, to tackle this problem, Li et al [50] proposed a discriminative low-rank analysis-synthesis dictionary learning (LR-ASDL) algorithm with the adaptively ordinal locality.…”

Section: Introductionmentioning

confidence: 99%

Locality Constraint Dictionary Learning with Support Vector for Pattern Classification

Yin

¹

,

Wu

²

,

Chen

³

2019

Preprint

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Discriminative dictionary learning (DDL) has recently gained significant attention due to its impressive performance in various pattern classification tasks. However, the locality of atoms is not fully explored in conventional DDL approaches which hampers their classification performance. In this paper, we propose a locality constraint dictionary learning with support vector discriminative term (LCDL-SV), in which the locality information is preserved by employing the graph Laplacian matrix of the learned dictionary. To jointly learn a classifier during the training phase, a support vector discriminative term is incorporated into the proposed objective function. Moreover, in the classification stage, the identity of test data is jointly determined by the regularized residual and the learned multi-class support vector machine. Finally, the resulting optimization problem is solved by utilizing the alternative strategy. Experimental results on benchmark databases demonstrate the superiority of our proposed method over previous dictionary learning approaches on both hand-crafted and deep features. The source code of our proposed LCDL-SV is accessible at https://github.com/yinhefeng/LCDL-SV.

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Low-rank analysis–synthesis dictionary learning with adaptively ordinal locality

Cited by 18 publications

References 45 publications

Discriminative Dictionary Pair Learning for Image Classification

Discriminative Dictionary Pair Learning for Image Classification

Locality Constraint Dictionary Learning With Support Vector for Pattern Classification

Locality Constraint Dictionary Learning with Support Vector for Pattern Classification

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