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Research on cooperative classification of multimedia visual images based on deep machine learning model

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Abstract

Aiming at the low accuracy of multimedia visual image cooperative classification, a new method of multimedia visual image cooperative classification based on depth machine learning model is proposed. Firstly, HSV color space model is selected to extract color features of multimedia visual images, Gabor function is used to extract texture features, and shape invariant moments are used to extract shape features. Then the features of multimedia visual images are recognized and classified, and the model parameters are optimized and adjusted according to the deviation of training output. The experimental results show that the average accuracy of this method is 95.892%, and the classification efficiency is high. The classification accuracy of this method is basically above 95%, and the classification accuracy is high. The training time of image type samples is 19 s, the testing time of image type is 12 s, and the time consumption is low.

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References

  1. Abdelgayed TS, Morsi WG, Sidhu TS (2017) Fault detection and classification based on co-training of semi-supervised machine learning. IEEE Transactions on Industrial Electronics, PP(99):1–1

  2. Ao L, Wu Z, Lu H et al (2018) Collaborative self-regression method with nonlinear feature based on multi-task learning for image classification. IEEE Access, PP (99):1–1

  3. Dao M, Nguyen NH, Nasrabadi NM et al (2016) Collaborative multi-sensor classification via sparsity-based representation. IEEE Trans Signal Process 64(9):2400–2415

    Article  MathSciNet  Google Scholar 

  4. Hu XP (2016) Multi-feature fusion image classification based on multi-core learning. Computer Engineering and Applications 52(5):194–198

    Google Scholar 

  5. Imani M, Ghassemian H (2016) Edge patch image-based morphological profiles for classification of multispectral and hyperspectral data. IET Image Process 11(3):164–172

    Article  Google Scholar 

  6. Imani M, Ghassemian H (2017) Edge-preserving-based collaborative representation for spectral-spatial classification. Int J Remote Sens 38(20):5524–5545

    Article  Google Scholar 

  7. Kowsari K, Heidarysafa M, Brown DE et al (2018) RMDL: Random Multimodel Deep Learning for Classification 12(7):65–79

    Google Scholar 

  8. Liu R, Gillies DF (2016) Overfitting in linear feature extraction for classification of high-dimensional image data. Pattern Recogn 53(C):73–86

    MATH  Google Scholar 

  9. Liu Z, Song L, Zhao W (2016) Classification of large-scale stellar spectra based on the non-linearly assembling learning machine. Mon Not R Astron Soc 455(4):4289–4294

    Article  Google Scholar 

  10. Lu C, Yan S, Lin Z (2016) Convex sparse spectral clustering: single-view to multi-view. IEEE Trans Image Process 25(6):2833–2843

    Article  MathSciNet  Google Scholar 

  11. Mishra M, Rout PK (2018) Detection and classification of micro-grid faults based on hht and machine learning techniques. Iet Generation Transmission & Distribution 12(2):388–397

    Article  Google Scholar 

  12. Murugan P (2018) Implementation of. Deep Convolutional Neural Network in Multi-class Categorical Image Classification 72(1):85–92

    Google Scholar 

  13. Na JH, Chang HJ (2016) Blockwise collaborative representation-based classification via l 2 -norm of query data for accurate face recognition. Electron Lett 52(13):1114–1116

    Article  Google Scholar 

  14. Ran Q, Wei L, Qian D (2018) Kernel one-class weighted sparse representation classification for change detection. Remote Sensing Letters 9(6):597–606

    Article  Google Scholar 

  15. Wang J, Jiao L (2017) Application of a homogenous patch mean kernel with within-class collaborative representation for hyperspectral imagery classification. Remote Sensing Letters 8(1):11–20

    Article  Google Scholar 

  16. Wang X, Yiu SM (2016) A multi-task learning model for malware classification with useful file access pattern from. API call sequence 34(5):133–142

    Google Scholar 

  17. Wang Y, Lin X, Wu L, et al (2017) Effective multi-query expansions: collaborative deep networks based feature learning for robust landmark retrieval. IEEE Transactions on Image Processing, PP(99):1–1

  18. Yeh CK, Wu WC, Ko WJ et al (2017) Learning Deep Latent Spaces for Multi-Label Classification 12(4):513–524

    Google Scholar 

  19. Zhang Y, Yang H (2017) Image classification method based on optimized visual bag model. Computer Application 37(8):2244–2247

    Google Scholar 

  20. Zhao Y, Ding Y, Zhao XY (2016) Image quality assessment based on complementary local feature extraction and quantification. Electron Lett 52(22):1849–1851

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. College of Art Design and Architecture, Liaoning University of Technology, Jinzhou, 121000, Liaoning, China

    Shu-yi Yuchi

  2. College of Information and Electronic Engineering, Hunan City University, Yiyang, 413000, Hunan, China

    Shu Xu

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  1. Shu-yi Yuchi
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  2. Shu Xu
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Correspondence to Shu Xu.

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Yuchi, Sy., Xu, S. Research on cooperative classification of multimedia visual images based on deep machine learning model. Multimed Tools Appl 80, 22657–22670 (2021). https://doi.org/10.1007/s11042-019-7637-x

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  • DOI: https://doi.org/10.1007/s11042-019-7637-x

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