Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification

Song, Jianqiang; Wang, Lin; Liu, Zuozhi; Liu, Muhua; Zhang, Mingchuan; Wu, Qingtao

doi:10.3390/app11167701

Open AccessArticle

Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification

by

Jianqiang Song

^1,2,

Lin Wang

¹,

Zuozhi Liu

³,

Muhua Liu

²

,

Mingchuan Zhang

¹

and

Qingtao Wu

^1,*

¹

School of Information Engineering, Henan University of Science and Technology, No. 263 Kaiyuan Avenue, Luoyang 471023, China

²

Control Science and Engineering Postdoctoral Mobile Station, Henan University of Science and Technology, No. 263 Kaiyuan Avenue, Luoyang 471023, China

³

School of Mathematics and Statistics, Guizhou University of Finance and Economics, Guiyang 550025, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2021, 11(16), 7701; https://doi.org/10.3390/app11167701

Submission received: 5 July 2021 / Revised: 13 August 2021 / Accepted: 17 August 2021 / Published: 21 August 2021

(This article belongs to the Section Computing and Artificial Intelligence)

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Abstract

Dictionary learning has been an important role in the success of data representation. As a complete view of data representation, hybrid dictionary learning (HDL) is still in its infant stage. In previous HDL approaches, the scheme of how to learn an effective hybrid dictionary for image classification has not been well addressed. In this paper, we proposed a locality preserving and label-aware constraint-based hybrid dictionary learning (LPLC-HDL) method, and apply it in image classification effectively. More specifically, the locality information of the data is preserved by using a graph Laplacian matrix based on the shared dictionary for learning the commonality representation, and a label-aware constraint with group regularization is imposed on the coding coefficients corresponding to the class-specific dictionary for learning the particularity representation. Moreover, all the introduced constraints in the proposed LPLC-HDL method are based on the

l_{2}

-norm regularization, which can be solved efficiently via employing an alternative optimization strategy. The extensive experiments on the benchmark image datasets demonstrate that our method is an improvement over previous competing methods on both the hand-crafted and deep features.

Keywords: locality preserving; label-aware constraint; hybrid dictionary learning; image classification

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MDPI and ACS Style

Song, J.; Wang, L.; Liu, Z.; Liu, M.; Zhang, M.; Wu, Q. Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification. Appl. Sci. 2021, 11, 7701. https://doi.org/10.3390/app11167701

AMA Style

Song J, Wang L, Liu Z, Liu M, Zhang M, Wu Q. Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification. Applied Sciences. 2021; 11(16):7701. https://doi.org/10.3390/app11167701

Chicago/Turabian Style

Song, Jianqiang, Lin Wang, Zuozhi Liu, Muhua Liu, Mingchuan Zhang, and Qingtao Wu. 2021. "Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification" Applied Sciences 11, no. 16: 7701. https://doi.org/10.3390/app11167701

APA Style

Song, J., Wang, L., Liu, Z., Liu, M., Zhang, M., & Wu, Q. (2021). Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification. Applied Sciences, 11(16), 7701. https://doi.org/10.3390/app11167701

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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Locality Preserving and Label-Aware Constraint-Based Hybrid Dictionary Learning for Image Classification

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