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Deep discriminative dictionary pair learning for image classification

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Abstract

Discriminative dictionary learning has been extensively used for pattern classification tasks. By incorporating different kinds of label information into the dictionary learning framework, a dictionary can be attained that represents the original signal with discriminative reconstruction. The previous works learn the dictionary in the original space which limits the dictionary learning performance. In this paper, we propose an approach, namely Deep Discriminative Dictionary Pair Learning (D\(^3\)PL) for image classification. The input of D\(^3\)PL is not the matrix collected by original gray images or hand-crafted features but the relatively deeper features derived from autoencoders. Then, a structured dictionary is designed based on the discriminative contributions across different classes to reconstruct the deep feature. In addition, the associated structured projective dictionary is learned as well to guarantee the decoders updating towards the minimal error of deconvolution operator. By leveraging the discriminative-dictionary-learning-based loss function and the autoencoder loss function, D\(^3\)PL can simultaneously learn the deep potential feature and the corresponding dictionary pair. In the testing phase of D\(^3\)PL, the minimum error between the deep feature and the structured projective component with regard to different classes can directly indicate the label by a basic matrix multiplication operation. Experimental results on challenging Extended Yale B, AR, UMIST, COIL20, Scene 15, and Caltech101 datasets demonstrate that the proposed D\(^3\)PL outperforms the prominent dictionary learning methods.

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References

  1. Budhiraja S, Sharma R, Agrawal S, Sohi BS (2021) Infrared and visible image fusion using modified spatial frequency-based clustered dictionary. Appl Intell 24:575–589

    Google Scholar 

  2. Hu Y, Zhang B, Jiang C, Chen Z (2022) A feature-level full-reference image denoising quality assessment method based on joint sparse representation. Appl Intell. https://doi.org/10.1007/s10489-021-03052-4

  3. Simsek M, Polat E (2021) Performance evaluation of pan-sharpening and dictionary learning methods for sparse representation of hyperspectral super-resolution. Signal, Image Video Process 15:1099–1106

    Article  Google Scholar 

  4. Liao M, Fan X, Li Y, Gao M (2023) Noise-related face image recognition based on double dictionary transform learning. Inform Sci 630:98–118

    Article  Google Scholar 

  5. Dawn DD, Khan A, Shaikh SH, Pal RK (2022) A dictionary based model for bengali document classification. Appl Intell. https://doi.org/10.1007/s10489-022-03955-w

  6. Ghasemi M, Kelarestaghi M, Eshghi F, Sharifi A (2022) D3fc: deep feature-extractor discriminative dictionary-learning fuzzy classifier for medical imaging. Appl Intell 52:7201–7217

    Article  Google Scholar 

  7. Singh UP, Singh KP, Thakur M (2022) Meta-dzsl: a meta-dictionary learning based approach to zero-shot recognition. Appl Intell 52:15938–15960

    Article  Google Scholar 

  8. Kang B, Zhu WP, Liang D, Chen M (2019) Robust visual tracking via nonlocal regularized multi-view sparse representation. Pattern Recogn 88:75–89

    Article  Google Scholar 

  9. Lan X, Ma AJ, Yuen PC, Chellappa R (2015) Joint sparse representation and robust feature-level fusion for multi-cue visual tracking. IEEE Trans Image Process 24(12):5826–5841

    Article  MathSciNet  MATH  Google Scholar 

  10. Elhamifar E, Vidal R (2013) Sparse subspace clustering: algorithm, theory, and applications. IEEE Trans Pattern Anal Mach Intell 35(11):2765–2781

    Article  Google Scholar 

  11. Li CG, You C, Rene V (2017) Structured sparse subspace clustering: a joint affinity learning and subspace clustering framework. IEEE Trans Image Process 26(6):2988–3001

    Article  MathSciNet  MATH  Google Scholar 

  12. Abdolali M, Gillis N, Rahmati M (2019) Scalable and robust sparse subspace clustering using randomized clustering and multilayer graphs. Signal Process 163:166–180

    Article  Google Scholar 

  13. Xu G, Yang M, Wu Q (2019) Sparse subspace clustering with lowrank transformation. Neural Comput Appl 31:3141–3154

    Article  Google Scholar 

  14. Wright J, Yang AY, Ganesh A, Sastry SS, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227

    Article  Google Scholar 

  15. Aharon M, Elad M, Bruckstein A (2006) K-svd: An algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans Signal Process 54(11):4311–4322

    Article  MATH  Google Scholar 

  16. Zhang Q, Li B (2010) Discriminative k-svd for dictionary learning in face recognition

  17. Jiang Z, Lin Z, Davis LS (2013) Label consistent k-svd: learning a discriminative dictionary for recognition. IEEE Trans Pattern Anal Mach Intell 35(11):2651–2664

    Article  Google Scholar 

  18. Wang Z, She H, Zhang Y, Du YP (2023) Parallel non-cartesian spatialtemporal dictionary learning neural networks (stdlnn) for accelerating 4d-mri. Med Image Anal 84:102701

    Article  Google Scholar 

  19. Sreekanth VS, Raghunath K, Mishra D (2023) Multi-resolution dictionary learning for discrimination of hidden features: A case study of atmospheric gravity waves. Signal Process 204:108831

    Article  Google Scholar 

  20. Shan D, Li D (2022) Semi-sparse residual recurrent neural network via dictionary representation for throat microphone quality enhancement. Appl Soft Comput 129:109618

    Article  Google Scholar 

  21. Zemouri R, Ibrahim R, Tahan A (2023) Hydrogenerator early fault detection: Sparse dictionary learning jointly with the variational autoencoder. Eng Appl Artif Intell 120:105859

    Article  Google Scholar 

  22. Golts A, Elad M (2016) Linearized kernel dictionary learning. IEEE J Selected Topics Signal Process 10(4):726–739

    Article  Google Scholar 

  23. Sun Y, Quan Y, Fu J (2018) Sparse coding and dictionary learning with class-specific group sparsity. Neural Comput Appl 30:1265–1275

    Article  Google Scholar 

  24. Zhang Z et al (2018) Jointly learning structured analysis discriminative dictionary and analysis multiclass classifier. IEEE Trans Neural Netw Learn Syst 29(8):3798–3814

    Article  MathSciNet  Google Scholar 

  25. Chang H, Tang H, Zhang F, Chen Y, Zheng H (2019) Graphregularized discriminative analysis-synthesis dictionary pair learning for image classification. IEEE Access 7:55398–55406

    Article  Google Scholar 

  26. Wang Y, Shao S, Xu R, Liu W, Liu B (2020) Class specific or shared? a cascaded dictionary learning framework for image classification. Signal Process 176:107697

    Article  Google Scholar 

  27. Lu J, Wang G, Zhou J (2017) Simultaneous feature and dictionary learning for image set based face recognition. IEEE Trans Image Process 26(8):4042–4054

    Article  MathSciNet  MATH  Google Scholar 

  28. Mairal J, Bach F, Ponce J, Sapiro G, Zisserman A (2008) Supervised dictionary learning

  29. Mairal J, Bach F, Ponce J (2010) Task-driven dictionary learning. IEEE Trans Pattern Anal Mach Intell 34(4):791–804

    Article  Google Scholar 

  30. Kviatkovsky I, Gabel M, Rivlin E, Shimshoni I (2016) On the equivalence of the lc-ksvd and the d-ksvd algorithms. IEEE Trans Pattern Anal Mach Intell 39(2):411–416

    Article  Google Scholar 

  31. Yang M, Zhang L, Feng X, Zhang D (2011) Fisher discrimination dictionary learning for sparse representation

  32. Zhu W, Yan Y, Peng Y (2016) Dictionary learning based on discriminative energy contribution for image classification. Knowledge-Based Syst 113:116–124

    Article  Google Scholar 

  33. Wang Z, Yang J, Nasrabadi N, Huang T (2013) A max-margin perspective on sparse representation-based classification

  34. Gu S, Zhang L, Zuo W, Feng X (2014) Projective dictionary pair learning for pattern classification

  35. Zhang Z, et al. (2019) Learning structured twin-incoherent twin-projective latent dictionary pairs for classification, 836-845

  36. Zhang Z et al (2021) Twin-incoherent self-expressive locality-adaptive latent dictionary pair learning for classification. IEEE Trans Neural Netw Learn Syst 323:947–961

  37. Tariyal S, Majumdar A, Singh R, Vatsa M (2016) Deep dictionary learning. IEEE Access 4:10096–10109

    Article  Google Scholar 

  38. Singhal V, Aggarwal HK, Tariyal S, Majumdar A (2017) Discriminative robust deep dictionary learning for hyperspectral image classification. IEEE Trans Geoscience Remote Sens 55(9):5274–5283

  39. Pang, X, Yang C, Zhang Z, You X, Zhang C (2019) A channel-blind decoding for ldpc based on deep learning and dictionary learning, 284-289

  40. Zhang, L, Yang M, Feng X (2011) Sparse representation or collaborative representation: which helps face recognition?

  41. Krizhevsky A, Sutskever I, Hinton G (2012) Imagenet classification with deep convolutional neural networks

  42. Elhamifar E, Vidal R (2011) Robust classification using structured sparse representation

  43. Michael G, Stephen B (2013) Cvx: Matlab software for disciplined convex programming, version 2.0 beta

  44. Cai S, Zuo W, Zhang L, Feng X, Wang P (2014) Support vector guided dictionary learning

  45. Wang Y, Du H, Zhang Y, Zhang Y (2021) Efficient and robust discriminant dictionary pair learning for pattern classification. Digital Signal Process 118:103227

    Article  Google Scholar 

  46. Dong J, Yang L, Liu C, Cheng W, Wang W (2022) Support vector machine embedding discriminative dictionary pair learning for pattern classification. Neural Netw 155:498–511

    Article  Google Scholar 

  47. Ji P, Zhang T, Li H, Salzmann M, Reid I (2017) Deep subspace clustering networks

  48. Zhang J, et al. (2019) Self-supervised convolutional subspace clustering network, 5468-5477

Download references

Acknowledgements

This research was partially supported by the National Key Research and Development Program of China under Grant No. 2021YFC3340402, the Key Research and Development Project of Zhejiang Province under Grant No. 2021C03151, Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ20F030015, the Fundamental Research Funds for the Provincial Universities of Zhejiang under Grant No. 2022YW40, and China Jiliang University Student Research Program No. 2022X25037.

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Authors and Affiliations

  1. Key Laboratory of Electromagnetic Wave Information Technology and Metrology of Zhejiang Province, College of Information Engineering, China Jiliang University, 310018, Hangzhou, Zhejiang, China

    Wenjie Zhu & Chunchun Chen

  2. The University of Queensland, St. Lucia, 4072, Brisbane, Australia

    Bo Peng

  3. College of Sciences, China Jiliang University, 310018, Hangzhou, Zhejiang, China

    Hao Chen

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  1. Wenjie Zhu
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  2. Bo Peng
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Correspondence to Wenjie Zhu.

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Wenjie Zhu and Bo Peng contributed equally to this work.

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Zhu, W., Peng, B., Chen, C. et al. Deep discriminative dictionary pair learning for image classification. Appl Intell 53, 22017–22030 (2023). https://doi.org/10.1007/s10489-023-04708-z

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