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Structured analysis dictionary learning based on discriminative Fisher pair

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Abstract

In analysis dictionary learning (ADL) algorithms, the row vectors (profiles) of the analysis coefficient matrix and analysis atoms are always one-to-one correspondence, and the analysis information of atoms could be represented by their corresponding profiles. However, the analysis atoms and their corresponding profiles are seldom jointly explored to formulate a discrimination term. In this paper, we exploit the analysis atoms and profiles to design a structured discriminative ADL algorithm for image classification, called structured analysis dictionary learning based on discriminative Fisher pair (SADL-DFP). Specifically, we explicitly provide the definitions of the profile and the newly defined profile block, which are used to illustrate the analysis mechanism of the ADL model. Then, the discriminative Fisher pair (DFP) model is designed by using the Fisher criterion of analysis atoms and profiles, which can enhance the inter-class separability and intra-class compactness of the analysis atoms and profiles. Since the profiles and analysis atoms can be updated alternatively and interactively, our DFP model can further encourage the analysis atoms to analyze the same-class training samples as much as possible. In addition, a robust multiclass classifier is simultaneously learned by utilizing the label information of the training samples and analysis atoms in our SADL-DFP algorithm. The experimental results show that the proposed SADL-DFP algorithm can outperform many state-of-the-art dictionary learning algorithms on multiple datasets with both deep learning-based features and hand-crafted features.

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References

  • 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 

  • Akhtar N, Mian A, Porikli F (2017) Joint discriminative Bayesian dictionary and classifier learning. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 1193–1202

  • Bao C, Ji H, Quan Y, Shen Z (2015) Dictionary learning for sparse coding: Algorithms and convergence analysis. IEEE Trans Pattern Anal Mach Intell 38(7):1356–1369

    Article  Google Scholar 

  • Cai X, Nie F, Huang H (2013) Exact top-k feature selection via l2, 0-norm constraint. In: Twenty-third international joint conference on artificial intelligence, ACM, pp 1240–1246

  • Cheng X, Zhang Y, Zhou L, Lu G (2021) Online dual dictionary learning for visual object tracking. J Ambient Intell Human Comput. https://doi.org/10.1007/s12652-020-02799-x

  • Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255

  • Donahue J, Jia Y, Vinyals O, Hoffman J, Zhang N, Tzeng E, Darrell T (2014) Decaf: a deep convolutional activation feature for generic visual recognition. In: International conference on machine learning. ACM, pp 647–655

  • Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A (2010) The pascal visual object classes (VOC) challenge. Int J Comput Vision 88(2):303–338

    Article  Google Scholar 

  • Fei-Fei L, Fergus R, Perona P (2004) Learning generative visual models from few training examples: an incremental Bayesian approach tested on 101 object categories. In: IEEE conference on computer vision and pattern recognition workshop. IEEE, pp 178

  • Foroughi H, Ray N, Zhang H (2017) Object classification with joint projection and low-rank dictionary learning. IEEE Trans Image Process 27(2):806–821

    Article  MathSciNet  MATH  Google Scholar 

  • Griffin G, Holub A, Perona P (2007) Caltech-256 object category dataset. California Institute of Technology, Pasadena

    Google Scholar 

  • Gu S, Zhang L, Zuo W, Feng X (2014) Projective dictionary pair learning for pattern classification. In: Advances in neural information processing systems, pp 793–801

  • Guo J, Guo Y, Kong X, Zhang M, He R (2016) Discriminative analysis dictionary learning. In: AAAI conference on artificial intelligence, pp 1617–1623

  • Hoi SC, Liu W, Lyu MR, Ma WY (2006) Learning distance metrics with contextual constraints for image retrieval. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 2072–2078

  • Hou C, Nie F, Li X, Yi D, Wu Y (2013) Joint embedding learning and sparse regression: a framework for unsupervised feature selection. IEEE Trans Cybern 44(6):793–804

    Google Scholar 

  • Hu J, Tan YP (2018) Nonlinear dictionary learning with application to image classification. Pattern Recogn 75:282–291

    Article  Google Scholar 

  • Huang GB, Ramesh M, Berg T, Learned-Miller E (2007) Labeled faces in the wild: a database for studying face recognition in unconstrained environments. University of Massachusetts, Amherst, Technical Report 07-49, Oct 2007

  • Huang KK, Dai DQ, Ren CX, Lai ZR (2016) Learning kernel extended dictionary for face recognition. IEEE Trans Neural Netw Learn Syst 28(5):1082–1094

    Article  Google Scholar 

  • 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 

  • Jiang W, Zhang Z, Qin J, Zhao M, Li F, Yan S (2017) Robust projective dictionary learning by joint label embedding and classification. In: IEEE international conference on data mining workshops. IEEE, pp 510–517

  • Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. Adv Neural Inf Process Syst 25:1097–1105

    Google Scholar 

  • Li Z, Lai Z, Xu Y, Yang J, Zhang D (2015) A locality-constrained and label embedding dictionary learning algorithm for image classification. IEEE Trans Neural Netw Learn Syst 28(2):278–293

    Article  MathSciNet  Google Scholar 

  • Li Z, Zhang Z, Fan Z, Wen J (2018) An interactively constrained discriminative dictionary learning algorithm for image classification. Eng Appl Artif Intell 72:241–252

    Article  Google Scholar 

  • Lu C, Shi J, Jia J (2013) Scale adaptive dictionary learning. IEEE Trans Image Process 23(2):837–847

    Article  MathSciNet  MATH  Google Scholar 

  • Martinez AM (1998) The ar face database. CVC Technical Report 24, Barcelona, Spain

  • Mi JX, Fu Q, Li W (2017) Adaptive class preserving representation for image classification. In: IEEE conference on computer vision and pattern recognition, IEEE, pp 7427–7435

  • Mi JX, Zhang YN, Lai Z, Li W, Zhou L, Zhong F (2019) Principal component analysis based on nuclear norm minimization. Neural Netw 118:1–16

    Article  MATH  Google Scholar 

  • Nam S, Davies ME, Elad M, Gribonval R (2013) The cosparse analysis model and algorithms. Appl Comput Harmon Anal 34(1):30–56

    Article  MathSciNet  MATH  Google Scholar 

  • Ni T, Gu X, Jiang Y (2020) Transfer discriminative dictionary learning with label consistency for classification of eeg signals of epilepsy. J Ambient Intell Human Comput:1–12

  • Sadeghi M, Babaie-Zadeh M, Jutten C (2013) Learning over complete dictionaries based on atom-by-atom updating. IEEE Trans Signal Process 62(4):883–891

    Article  MATH  Google Scholar 

  • Shekhar S, Patel VM, Chellappa R (2014) Analysis sparse coding models for image-based classification. In: IEEE international conference on image processing. IEEE, pp 5207–5211

  • Shen F, Shen C, Liu W, Tao Shen H (2015) Supervised discrete hashing. In: IEEE conference on computer vision and pattern recognition, pp 37–45

  • Simon M, Rodner E (2015) Neural activation constellations: unsupervised part model discovery with convolutional networks. In: IEEE International Conference on Computer Vision, IEEE, pp 1143–1151

  • Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:14091556

  • Tang H, Liu H, Xiao W, Sebe N (2020) When dictionary learning meets deep learning: deep dictionary learning and coding network for image recognition with limited data. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.2997289

  • Tommasi T, Tuytelaars T (2014) A testbed for cross-dataset analysis. In: European Conference on Computer Vision Workshops. Springer, pp 18–31

  • Vu TH, Monga V (2017) Fast low-rank shared dictionary learning for image classification. IEEE Trans Image Process 26(11):5160–5175

    Article  MathSciNet  MATH  Google Scholar 

  • Wang J, Guo Y, Guo J, Li M, Kong X (2017a) Synthesis linear classifier based analysis dictionary learning for pattern classification. Neurocomputing 238:103–113

    Article  Google Scholar 

  • Wang J, Guo Y, Guo J, Luo X, Kong X (2017b) Class-aware analysis dictionary learning for pattern classification. IEEE Signal Process Lett 24(12):1822–1826

    Article  Google Scholar 

  • Wang K, Lin L, Zuo W, Gu S, Zhang L (2016) Dictionary pair classifier driven convolutional neural networks for object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, IEEE, pp 2138–2146

  • Wang SJ, Yang J, Sun MF, Peng XJ, Sun MM, Zhou CG (2012) Sparse tensor discriminant color space for face verification. IEEE Trans Neural Netw Learn Syst 23(6):876–888

    Article  Google Scholar 

  • Wang X, Gu Y (2017) Cross-label suppression: a discriminative and fast dictionary learning with group regularization. IEEE Trans Image Process 26(8):3859–3873

    Article  MathSciNet  MATH  Google Scholar 

  • Wen Z, Hou B, Jiao L (2016) Discriminative dictionary learning with two-level low rank and group sparse decomposition for image classification. IEEE Trans Cybern 47(11):3758–3771

    Article  Google Scholar 

  • Wu X, Li Q, Xu L, Chen K, Yao L (2017) Multi-feature kernel discriminant dictionary learning for face recognition. Pattern Recogn 66:404–411

    Article  Google Scholar 

  • Xie GS, Zhang Z, Liu L, Zhu F, Zhang XY, Shao L, Li X (2019) Srsc: Selective, robust, and supervised constrained feature representation for image classification. IEEE Trans Neural Netw Learn Syst 31(10):4290–4302

    Article  MathSciNet  Google Scholar 

  • Xu M, Dong H, Chen C, Li L (2016) Unsupervised dictionary learning with fisher discriminant for clustering. Neurocomputing 194:65–73

    Article  Google Scholar 

  • Xu Y, Li Z, Zhang B, Yang J, You J (2017) Sample diversity, representation effectiveness and robust dictionary learning for face recognition. Inf Sci 375:171–182

    Article  Google Scholar 

  • Yaghoobi M, Blumensath T, Davies ME (2009) Dictionary learning for sparse approximations with the majorization method. IEEE Trans Signal Process 57(6):2178–2191

    Article  MathSciNet  MATH  Google Scholar 

  • Yang M, Zhang L, Feng X, Zhang D (2014) Sparse representation based fisher discrimination dictionary learning for image classification. Int J Comput Vision 109(3):209–232

    Article  MathSciNet  MATH  Google Scholar 

  • Yang M, Chang H, Luo W, Yang J (2017) Fisher discrimination dictionary pair learning for image classification. Neurocomputing 269:13–20

    Article  Google Scholar 

  • Zeiler MD, Fergus R (2014) Visualizing and understanding convolutional networks. In: European Conference on Computer Vision. Springer, pp 818–833

  • Zhang Z, Jiang W, Qin J, Zhang L, Li F, Zhang M, Yan S (2017) Jointly learning structured analysis discriminative dictionary and analysis multiclass classifier. IEEE Trans Neural Netw Learn Syst 29(8):3798–3814

    Article  MathSciNet  Google Scholar 

  • Zhang Z, Liu L, Shen F, Shen HT, Shao L (2018) Binary multi-view clustering. IEEE Trans Pattern Anal Mach Intell 41(7):1774–1782

    Article  Google Scholar 

  • Zhang Z, Liu L, Luo Y, Huang Z, Shen F, Shen HT, Lu G (2020) Inductive structure consistent hashing via flexible semantic calibration. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2020.3046924

  • Zhang Z, Zhu X, Lu G, Zhang Y (2021) Probability ordinal-preserving semantic hashing for large-scale image retrieval. ACM Trans Knowl Discov Data 15(3):37

    Article  Google Scholar 

Download references

Acknowledgements

The work is supported by the Natural Science Foundation of China (U170126, 61672365, 62002085), Science and Technology Program of Guangzhou (201804010355, 201805010001) and Science and Technology Planning Project of Guangdong Province (2018B030322016), and also supported by the Youth Innovation Project of the Department of Education of Guangdong Province (2020KQNCX040), and Special Projects for Key Fields in Higher Education of Guangdong, China (2020ZDZX2002, 2020ZDZX3077).

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Authors and Affiliations

  1. Industrial Training Center, Guangdong Polytechnic Normal University, Guangzhou, 510665, China

    Zhengming Li, Ruijun Ma & Dan Xiang

  2. School of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, 518055, China

    Zheng Zhang

  3. Department of Mathematics, University of Leicester, Lecicester, LE1 7RH, UK

    Shuihua Wang

  4. Guangdong Provincial Key Laboratory of Intellectual Property and Big Data, Guangzhou, China

    Fangyuan Lei

  5. Department of Computer and Information Science, University of Macau, Macau, 999078, China

    Zheng Zhang

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  1. Zhengming Li
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Correspondence to Zheng Zhang or Shuihua Wang.

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Li, Z., Zhang, Z., Wang, S. et al. Structured analysis dictionary learning based on discriminative Fisher pair. J Ambient Intell Human Comput 14, 5647–5664 (2023). https://doi.org/10.1007/s12652-021-03262-1

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