Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification

doi:10. 19682 / j. cnki. 1005-8885. 2023. 0010

中国邮电高校学报(英文) ›› 2023, Vol. 30 ›› Issue (5): 1-10.doi: 10. 19682 / j. cnki. 1005-8885. 2023. 0010

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Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification

彭宏¹,刘耀宗²

1. 文化和旅游部民族民间文艺发展中心
2. 北京邮电大学

收稿日期:2023-07-19 修回日期:2023-09-03 出版日期:2023-10-31 发布日期:2023-10-30
通讯作者: 彭宏 E-mail:466985365@qq.com
基金资助:
the National Key Research and Development Project (2021YFF0901701).

Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification

Hong PENG¹,yaozong liu²

1. Center for Ethnic and National Folk Literature and Art Development, Ministry of Culture and Tourism of the People's Republic of China, Beijing 100007, China
2. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China

Received:2023-07-19 Revised:2023-09-03 Online:2023-10-31 Published:2023-10-30
Contact: Hong PENG E-mail:466985365@qq.com
Supported by:
the National Key Research and Development Project (2021YFF0901701).

摘要/Abstract

摘要：

A multi-layer dictionary learning algorithm that joints global constraints and Fisher discrimination (JGCFD-MDL) for image classification tasks was proposed. The algorithm reveals the manifold structure of the data by learning the global constraint dictionary and introduces the Fisher discriminative constraint dictionary to minimize the intra-class dispersion of samples and increase the inter-class dispersion. To further quantify the abstract features that characterize the data, a multi-layer dictionary learning framework is constructed to obtain high-level complex semantic structures and improve image classification performance. Finally, the algorithm is verified on the multi-label dataset of court costumes in the Ming Dynasty and Qing Dynasty, and better performance is obtained. Experiments show that compared with the local similarity algorithm, the average precision is improved by 3.34% . Compared with the single-layer dictionary learning algorithm, the one-error is improved by 1.00% , and the average precision is improved by 0.54% . Experiments also show that it has better performance on general datasets.

关键词: global similarity, Fisher discrimination, joint local-constraint and Fisher discrimination based dictionary learning (JLCFDDL), joint global constraint and Fisher discrimination based multi-layer dictionary learning, image classification

Abstract:

Key words:

global similarity, Fisher discrimination, joint local-constraint and Fisher discrimination based dictionary learning (JLCFDDL), joint global constraint and Fisher discrimination based multi-layer dictionary learning, image classification

Hong PENG yaozong liu. Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification[J]. The Journal of China Universities of Posts and Telecommunications, 2023, 30(5): 1-10.

参考文献

[1] PENG Y L, LIU S G, WANG X, et al. Joint local constraint and Fisher discrimination based dictionary learning for image

classification. Neurocomputing, 2020, 398: 505 -519.

[2] LI Z M, LAI Z H, XU Y, et al. A locality-constrained and label embedding dictionary learning algorithm for image classification.

IEEE Transactions on Neural Networks and Learning Systems, 2015, 28(2): 278 -293.

[3] SONG J Q, XIE X M, SHI G M, et al. Multi-layer discriminative dictionary learning with locality constraint for image classification. Pattern Recognition, 2019, 91: 135 -146.

[4] SHABAN A, RABIEE H R, FARAJTABAR M, et al. From local similarity to global coding: an application to image classification. Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition, 2013, Jun 23 - 28, Portland, OR, USA.

Piscataway, NJ, USA: IEEE, 2013: 2794 -2801.

[5] DAVIS G, MALLAT S, AVELLANEDA M. Adaptive greedy approximations. Constructive Approximation, 1997, 13: 57 -98.

[6] MALLAT S G, ZHANG Z F. Matching pursuits with time-frequency dictionaries. IEEE Transactions on Signal Processing,

1993, 41(12): 3397 -3415.

[7] PATI Y C, REZAIIFAR R, KRISHNAPRASAD P S. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. Proceedings of the 27th Asilomar Conference on Signals, Systems and Computers, 1993, Nov 1 - 3, Pacific Grove, CA, USA. Piscataway, NJ, USA: IEEE, 1993: 40 -44.

[8] CHEN S S, DONOHO D L, SAUNDERS M A. Atomic decomposition by basis pursuit. SIAM Review, 2001, 43 (1): 129 -159.

[9] SHAO S, XU R, LIU W F, et al. Label embedded dictionary learning for image classification. Neurocomputing, 2020, 385:

122 -131.

[10] BAO C L, JI H, QUAN Y H, et al. L0 norm based dictionary learning by proximal methods with global convergence. Proceedings

of the 2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014, Jun 23 - 28, Columbus, OH, USA. Piscataway, NJ, USA: IEEE, 2014: 3858 -3865.

[11] WRIGHT J, YANG A Y, GANESH A, et al. Robust face recognition via sparse representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 31(2): 210 -227.

[12] AHARON M, ELAD M, BRUCKSTEIN A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation.

IEEE Transactions on Signal Processing, 2006, 54(11): 4311 -4322.

[13] ZHANG Q, LI B X. Discriminative K-SVD for dictionary learning in face recognition. Proceedings of the 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010, Jun 13 - 18, San Francisco, CA, USA. Piscataway, NJ, USA: IEEE, 2010: 2691 -2698.

[14] JIANG Z L, LIN Z, DAVIS L S. Label consistent K-SVD: learning a discriminative dictionary for recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(11): 2651 -2664.

[15] STEFEN C W T, ROMBAUT M, PELLERIN D. Multi-layer dictionary learning for image classification. Proceedings of the 17th International Conference on Advanced Concepts for Intelligent Vision Systems ( ACIVS'16), 2016, Oct 24 - 27, Lecce, Italy. LNIP 10016. Berlin, Germany: Springer, 2016: 522 -533.

[16] TANG H, LIU H, XIAO W, et al. When dictionary learning meets deep learning: deep dictionary learning and coding network for image recognition with limited data. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(5): 2129 -2141.

[17] BELKIN M, NIYOGI P. Laplacian eigenmaps and spectral techniques for embedding and clustering. Proceedings of the 14th

International Conference on Neural Information Processing Systems: Natural and Synthetic (NIPS'01), 2001, Dec 3 - 8, Vancouver, Canada. Cambridge, MA, USA: MIT Press, 2001: 585 -591.

[18] GHAMRAWI N, MCCALLUM A. Collective multi-label classification. Proceedings of the 14th ACM International Conference on Information and Knowledge Management, 2005, Oct 31 - Nov 5, Bremen, Germany, New York, NY, USA: ACM, 2005: 195 -200.

[19] TSOUMAKAS G, VLAHAVAS I. Random k-labelsets: an ensemble method for multilabel classification. Proceedings of the 18th European Conference on Machine Learning ( ECML'07), 2007, Sep 17 - 21, Warsaw, Poland. LNAI 4701. Berlin, Germany: Springer, 2007: 406 -417.

[20] ZHANG M L, ZHOU Z H. ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognition,2007,40(7):2038 -2048.

[21] HUANG J, LI G R, HUANG Q M, et al. Learning label-specific features and class-dependent labels for multi-label classification. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(12): 3309 -3323.

Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification

Joint global constraint and Fisher discrimination based multi-layer dictionary learning for image classification

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