Abstract
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
Highlights
Dictionary learning (DL) has aroused considerable interest during the past decade and has been adopted in a wide range of applications, such as face recognition [1], image fusion [2] and person re-identification [3], [4]
We propose a locality constraint dictionary learning with support vector discriminative term (LCDL-SV) for pattern classification, which belongs to the shared synthesis dictionary learning (SDL) category
To validate the effectiveness of employing both the regularized residual and support vector machine (SVM), we report the results of LCDL-SV only using regularized residual for classification and LCDL-SV only employing the learned multi-class SVM for classification, which are denoted by LCDL-SV (Res) and LCDL-SV (SVM), respectively
Summary
Dictionary learning (DL) has aroused considerable interest during the past decade and has been adopted in a wide range of applications, such as face recognition [1], image fusion [2] and person re-identification [3], [4]. According to the characteristic of the learned dictionary, existing DL approaches for pattern classification can be divided into three categories: synthesis dictionary learning (SDL), analysis dictionary learning (ADL) and dictionary pair learning (DPL). In SDL, the dictionary is employed to represent the input data as a linear superposition of atoms. ADL aims to yield the sparse representation by exploiting the dictionary as a transformation matrix. According to whether the dictionary is class-shared or not, SDL can be further divided into three different types, i.e., shared SDL, class-specific SDL
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