Laplacian regularized locality-constrained coding for image classification

Min, Huaqing; Liang, Mingjie; Luo, Ruiyan; Zhu, Jun

doi:10.1016/j.neucom.2015.07.084

Cited by 14 publications

(4 citation statements)

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“…In spatial pyramid matching sparse coding (SCSPM), authors [14] developed an extension of the SPM method, by generalising VQ to SC. In Laplacian regularised locality‐constrained coding [15], the dependency between local features was also considered, which improved classification accuracy. In LLC on the basis of histogram intersection, authors [16] use histogram intersection to describe the distance between dictionary and feature, achieving more robust codes.…”

Section: Introductionmentioning

confidence: 99%

KSR‐BOF: a new and exemplified method (as KSRs method) for image classification

Maleki

Hodtani

Hashemi

2020

IET Image Processing

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Section: Introductionmentioning

confidence: 99%

KSR‐BOF: a new and exemplified method (as KSRs method) for image classification

Maleki

Hodtani

Hashemi

2020

IET Image Processing

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“…Dong et al [20] proposed an orthonormal DL method by exerting an orthonormal constraint on the learned dictionary to enforce the dictionary atoms to be as dissimilar as possible. Min et al [21] constructed a Laplacian regularized locality-constrained coding (LapLLC) algorithm for image classification, in which the similarity matrix is defined on the training data. To fully exploit the locality and label information of the learned dictionary, Li et al [22] constructed a locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Locality Constraint Dictionary Learning With Support Vector for Pattern Classification

Yin

Chen

2019

IEEE Access

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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.

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“…However, such an approach cannot obtain a robust dictionary with which the coding coefficients of the target patches can be used as features to reliably distinguish the target from the background [13]. Data locality has also been observed as a key issue in clustering, dimensionality reduction [14], density estimation [15], and image classification [16], [17]. While sparse coding has been widely used for addressing image classification; whether sparsity alone is sufficient or even necessary for solving such tasks has been systematically investigated in [18], [19], [20].…”

Section: Introductionmentioning

confidence: 99%

Graph Regularized and Locality-Constrained Coding for Robust Visual Tracking

Zhou

Bhaskar

Liu

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Visual tracking is complicated due to factors such as occlusion, background clutter, abrupt target motion, and illumination variations, among others. In recent years, subspace representation and sparse coding techniques have demonstrated significant improvements in tracking. However, performance gain in tracking has been at the expense of losing locality and similarity attributes among the instances to be encoded. In this paper, a Graph Regularized and Locality-constrained Coding (GRLC) technique that encapsulates local manifold structure of the data in order to preserve locality and similarity information among instances, is proposed. The GRLC methodology incorporates a similarity-preserving term within the objective function of the locality-constrained linear coding model, thereby overcoming some of the inherent instability issues common to such coding methods. In the proposed GRLC scheme, a graph Laplacian regularizer is chosen as a smoothing operator in order to learn both the representation dictionary and the coefficients by preserving the local structure of the data. This graph Laplacian smoothing operator ensures that the representations vary smoothly along the geodesics of the data manifold. Thus by deriving the objective function of the GRLC method, a discriminative dictionary of instances can be iteratively obtained and the corresponding coefficients for each candidate can be computed using this learned dictionary. Finally, an effective observation likelihood function based on reconstruction error and a simple dictionary update scheme for visual target tracking are also proposed. Experimental results on the CVPR2013 Visual Tracker Benchmark have demonstrated favorable performance of the proposed technique both in terms of accuracy and robustness.

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Laplacian regularized locality-constrained coding for image classification

Cited by 14 publications

References 40 publications

KSR‐BOF: a new and exemplified method (as KSRs method) for image classification

KSR‐BOF: a new and exemplified method (as KSRs method) for image classification

Locality Constraint Dictionary Learning With Support Vector for Pattern Classification

Graph Regularized and Locality-Constrained Coding for Robust Visual Tracking

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