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Feature mining simulation of video image information in multimedia learning environment based on BOW algorithm

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Abstract

With the development of computer image processing technology, video image information feature mining in multimedia environment has become a research hotspot in the field of education with its unique characteristics. However, the current method is to classify and retrieve through the video image information features. When the video image information is disordered, the multimedia video image information features cannot be classified, and the criterion for measuring the size of the video image information cannot be given, and the information feature mining accuracy is low. Therefore, this paper proposes a new efficient algorithm for multimedia video image information retrieval. Firstly, the SIFT features of the video image are analysed to obtain the SIFT features of the video image. Secondly, the SIFT feature is used for feature matching to identify the target image. Finally, the BOW algorithm is introduced to index the matched SIFT features, and the bag-of-words model, TF-IDF weighting and Euclidean distance are used to complete the similarity calculation of the image, and the feature mining of multimedia video image information is completed. The simulation results show that the proposed method effectively improves the feature mining speed and feature mining accuracy and has better robustness.

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References

  1. Chi R, Lu ZM, Ji QG (2017) Real-time multi-feature based fire flame detection in video. IET Image Proc 11(1):31–37

    Article  Google Scholar 

  2. Chen M, Liu Q (2016) Blow-up criteria of smooth solutions to a 3D modelof electro-kinetic fluids in a bounded domain. Electron J Differ Equ 2016(128):1–8

    Google Scholar 

  3. Esmail M, Tzoneva R, Krishnamurthy S (2017) Review of automatic generation control in deregulated environment. IFAC-PapersOnLine 50(2):88–93

    Article  Google Scholar 

  4. Gerberry DJ (2018) An exact approach to calibrating infectious disease models to surveillance data: the case of HIV and HSV-2. Math Biosci Eng 15(1):153–179

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen M, Shengqi L, Liu Q (2018) Global regularity for a 2D model of electro-kinetic fluid in a bounded domain. Acta Math Appl Sin Engl Ser 34(2):398–403

    Article  MathSciNet  MATH  Google Scholar 

  6. Darwich A, Hébert PA, Bigand A et al (2018) Background subtraction based on a new fuzzy mixture of gaussians for moving object detection. J Imaging 4(7):92

    Article  Google Scholar 

  7. Ren J, Zhang C, Zhang L et al (2017) Automatic measurement of traffic state parameters based on computer vision for intelligent transportation surveillance. Int J Pattern Recognit Artif Intell 32(4):8

    Google Scholar 

  8. Sun J, Jia N (2015) Human target matching and tracking method in coal mine video. J China Univ Min Technol 44(3):540–548

    Google Scholar 

  9. Shengqi L, Chen M, Liu Q (2018) On regularity for an Ericksen–Leslie’s parabolic-hyperbolic liquid crystals model. ZAMM Z Angew Math Mech 98(9):1574–1584

    Article  MathSciNet  MATH  Google Scholar 

  10. Sarkar S, Venugopalan V, Reddy K et al (2017) Deep learning for automated occlusion edge detection in RGB-D frames. J Signal Process Syst 88(2):205–217

    Article  Google Scholar 

  11. Ling H, Cheng H, Ma Q et al (2012) Efficient image copy detection using multiscale fingerprints. IEEE Multimed 19(1):69

    Article  Google Scholar 

  12. Govender N, Torr P, Keaikitse M et al (2014) Probabilistic active recognition of multiple objects using hough-based geometric matching features. Cogn Syst Monogr 23:89–109

    Article  Google Scholar 

  13. Emile B (2008) Comparative study of global invariant descriptors for object recognition. J Electron Imaging 17(2):023015

    Article  Google Scholar 

  14. Zhou Z, Zhou M, Shi X (2016) Target tracking based on foreground probability. Multimed Tools Appl 75(6):3145–3160

    Article  Google Scholar 

  15. Shu C, Liang L, Liang W et al (2016) 3D pose tracking with multitemplate warping and SIFT correspondences. IEEE Trans Circuits Syst Video Technol 26(11):2043–2055

    Google Scholar 

  16. Ming Y, Hong X (2016) A unified 3D face authentication framework based on robust local mesh SIFT feature. Neurocomputing 184(C):117–130

    Article  Google Scholar 

  17. Griffin LD (2019) The atlas structure of images. IEEE Trans Pattern Anal Mach Intell 41(1):234–245

    Article  Google Scholar 

  18. Iftimi A, Montes F, Mateu J et al (2016) Measuring spatial inhomogeneity at different spatial scales using hybrids of Gibbs point process models. Stoch Environ Res Risk Assess 31(6):1–15

    Google Scholar 

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Authors and Affiliations

  1. College of Art and Design, Zhengzhou University of Aeronautics, Zhengzhou, 450015, Henan, China

    Lijuan Zhang

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  1. Lijuan Zhang
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Correspondence to Lijuan Zhang.

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Zhang, L. Feature mining simulation of video image information in multimedia learning environment based on BOW algorithm. J Supercomput 76, 6561–6578 (2020). https://doi.org/10.1007/s11227-019-02890-x

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  • DOI: https://doi.org/10.1007/s11227-019-02890-x

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