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Image recognition and classification with HOG based on nonlinear support tensor machine

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Abstract

Spatial structure information is very important in image analysis algorithms. Traditional machine learning methods based on vectorization strategies often ignore the spatial information of the original data, resulting in low image recognition and classification accuracy. Different from the vector representation, the tensor representation can preserve spatial structure information in still images. Histogram of oriented gradient (HOG) is a feature descriptor that generates histograms by calculating the gradient amplitude and direction of the local area of the image. This paper proposes a new support tensor machine learning method based on tensor space, where the HOG method in the form of tensors is used to extract the features of the image data. Borrowing the broadcasting idea, we investigate a flexible and concise nonparametric tensor model to capture the nonlinear spatial information in HOG tensor. By the polynomial spline approximation to nonparametric functions and low-rank CP decomposition, a new spline support tensor classification algorithm is studied with the alternating iterative approach under the framework of original support vector machine. The experimental result shows the superior performance of the proposed model compared with the existing methods.

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All data, models, or code generated or used during the study are available from the corresponding author by request.

References

  1. Carroll JD, Chang JJ (1970) Analysis of individual differences in multidimensional scaling via an n-way generalization of “eckart-young” decomposition. Psychometrika 35(3):283–319

    Article  MATH  Google Scholar 

  2. Chen C, Batselier K, Yu W, Wong N (2022) Kernelized support tensor train machines. Pattern Recog 122:108337

    Article  Google Scholar 

  3. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    Article  MATH  Google Scholar 

  4. Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. IEEE Comput Soc Conf Comput Vis Pattern Recog 1:886–893

    Google Scholar 

  5. Dang Y, Jiang N, Hu H, Ji Z, Zhang W (2018) Image classification based on quantum k-nearest-neighbor algorithm. Quant Inf Process 17:239–0

    Article  MATH  Google Scholar 

  6. Hao Z, He L, Chen B, Yang X (2013) A linear support higher-order tensor machine for classification. IEEE Trans Image Process Publ IEEE Signal Process Soc 22(7):2911–2920

    Google Scholar 

  7. Harshman RA (1970) Foundations of the parafac procedure : Models and conditions for an “explanatory” multimodal factor analysis. Ucla Work Pap Phon 16:1–84

    Google Scholar 

  8. He L, Kong X, Yu PS, Ragin AB, Yang X (2014) Dusk: A dual structure-preserving kernel for supervised tensor learning with applications to neuroimages. In: Proceedings of the 2014 SIAM International Conference on Data Mining (SDM), pp 127–135

  9. He L, Lu CT, Ding H, Wang S, Shen L, Yu PS, Ragin AB (2017) Multi-way multi-level kernel modeling for neuroimaging classification. Comput Vis Pattern Recog :6846–6854

  10. Jia X, Wang S, Yang Y (2018) Least-squares support vector machine for semi-supervised multi-tasking. :79–86

  11. Kayhan N, Fekri-Ershad S (2021) Content based image retrieval based on weighted fusion of texture and color features derived from modified local binary patterns and local neighborhood difference patterns. Multimed Tools Appl 80 (21–23):32763–32790

    Article  Google Scholar 

  12. Kiers H (2000) Towards a standardized notation and terminology in multiway analysis. J Chemometr 14:105–122

    Article  Google Scholar 

  13. Kolda T (2009) Tensor decompositions and applications. Siam Rev 51(3):455–500

    Article  MathSciNet  MATH  Google Scholar 

  14. Kolda TG, Gibson T (2006) Multilinear operators for higher-order decompositions. Off Sci Tech Inf Tech Rep

  15. Kossaifi J, Lipton ZC, Kolbeinsson A, Khanna A, Furlanello T, Anandkumar A (2020) Tensor regression networks. J Mach Learn Res 21(123):1–21

    MathSciNet  MATH  Google Scholar 

  16. Liu J, Wu Z, Xiao L, Sun J, Yan H (2020) Generalized tensor regression for hyperspectral image classification. IEEE Trans Geosci Remote Sens 58 (2):1244–1258

    Article  Google Scholar 

  17. Liu Y, Zhang H, Wu Y (2011) Hard or soft classification? large-margin unified machines. J Am Stat Assoc 106:166–177

    Article  MathSciNet  MATH  Google Scholar 

  18. Lu H, Plataniotis KN, Venetsanopoulos AN (2008) Mpca: Multilinear principal component analysis of tensor objects. IEEE Trans Neural Netw 19(1):18–39

    Article  Google Scholar 

  19. Ma G, He L, Lu CT, Yu PS, Ragin A (2016) Spatio-temporal tensor analysis for whole-brain fmri classification. In: SIAM International Conference on Data Mining (SDM), pp 819–827

  20. Mocks J (1988) Topographic components model for event-related potentials and some biophysical considerations. IEEE Trans Biomed Eng 35(6):482–484

    Article  Google Scholar 

  21. Oseledets IV (2011) Tensor-train decomposition. SIAM J Sci Comput 33(5):2295–2317

    Article  MathSciNet  MATH  Google Scholar 

  22. Reyes J, Stoudenmire M (2020) A multi-scale tensor network architecture for classification and regression. arXiv:2001.08286v1

  23. Schlkopf B, Herbrich R, Smola AJ (2001) A generalized representer theorem. Springer Berlin Heidelberg pp 416–426

  24. Signoretto M, Lathauwer LD, Suykens J (2011) A kernel-based framework to tensorial networks. Neural Netw Off J Int Neural Netw Soc 24(8):861–874

    Article  MATH  Google Scholar 

  25. Song L, Zhao X, Meng L, Ling J, Shi H (2013) Least squares support tensor machine. In: International symposium on operations research and its applications in engineering, pp 1–6

  26. Tan V, Dat T, Wanli M (2015) Tensor decomposition and application in image classification with histogram of oriented gradients. Neurocomputing 165 (1):38–45

    Google Scholar 

  27. Tao D, Li X, Wu X, Hu W, Maybank (2005) Supervised tensor learning. Knowl Inf Syst 13(1):450–457

    Google Scholar 

  28. Wang B, Zou H (2016) Sparse distance weighted discrimination. J Comput Graph Stat 25(3):826–838

    Article  MathSciNet  Google Scholar 

  29. Wang B, Zou H (2019) A multicategory kernel distance weighted discrimination method for multiclass classification. Technometrics 61(3):396–408

    Article  MathSciNet  Google Scholar 

  30. Wang C, Ning X, Sun L, Zhang L, Li W, Bai X (2022) Learning discriminative features by covering local geometric space for point cloud analysis. IEEE Trans Geosci Remote Sens 60:1–15

    Google Scholar 

  31. Wang C, Wang X, Zhang J, Zhang L, Bai X, Ning X, Zhou J, Hancock E (2022) Uncertainty estimation for stereo matching based on evidential deep learning. Pattern Recogn 124(C)

  32. Yan S, Xu D, Yang Q, Zhang L, Tang X, Zhang HJ (2007) Multilinear discriminant analysis for face recognition. IEEE Trans Image Process 16 (1):212–220

    Article  MathSciNet  Google Scholar 

  33. Zhang J, Han Y, Jiang J (2016) Tucker decomposition-based tensor learning for human action recognition. Multimed Syst 22:343–353

    Article  Google Scholar 

  34. Zhang J, Jiang J (2016) Decomposition-based tensor learning regression for improved classification of multimedia. J Vis Commun Image Represent 41:260–271

    Article  Google Scholar 

  35. Zhang J, Liu Y, Jiang J (2921) Tensor learning and automated rank selection for regression-based video classification. Multimed Tools Appl 77:3–29230

    Google Scholar 

  36. Zhao Q, Zhou G, Adalı T, Zhang L, Cichocki A (2013) Kernel-based tensor partial least squares for reconstruction of limb movements. In: 2013 IEEE international conference on acoustics, speech and signal processing, pp 3577–3581

  37. Zhao X, Deng N, Jing L (2017) Application of image recognition in civil aviation security based on tensor learning. J Intell Fuzzy Syst 33(4):2145–2157

    Article  Google Scholar 

  38. Zheng L, Huang H, Zhu C, Zhang K (2020) A tensor-based k -nearest neighbors method for traffic speed prediction under data missing. Transportmetrica B Transp Dyn 8:182–199

    Article  Google Scholar 

  39. Zhou Y, Wong R, He K (2020) Broadcasted nonparametric tensor regression. arXiv:2008.12927v1

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Acknowledgements

We sincerely thank three reviewers for their insightful comments that have led to significant improvement of this article.

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Correspondence to Weihua Zhao.

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This work was supported in part by the National Social Science Fund (22BTJ025) and in part by the National Natural Science Fund (11871411).

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Zhu, C., Zhao, W. & Lian, H. Image recognition and classification with HOG based on nonlinear support tensor machine. Multimed Tools Appl 82, 20119–20138 (2023). https://doi.org/10.1007/s11042-022-14320-x

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  • DOI: https://doi.org/10.1007/s11042-022-14320-x

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