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Sign Language Video Classification Based on Image Recognition of Specified Key Frames

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Multimedia Technology and Enhanced Learning (ICMTEL 2020)

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Abstract

This paper is based on the Chinese sign language video library, and discusses the algorithm design of video classification based on handshape recognition of key frames in video. Video classification in sign language video library is an important part of sign language arrangement and is also the premise of video feature retrieval. At present, sign language video’s handshape classification work is done manually. The accuracy and correctness of the results are quite erroneous and erroneous. In this paper, from the angle of computer image analysis, the definition and extraction of key frames are carried out, and then the region of interest is identified. Finally, an improved SURF algorithm is used to match the area of interest and the existing hand image, and the classification of the video is completed. The entire process is based on the actual development environment, and it can be used for reference based on the classification of video image features.

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References

  1. Johnson, A.Y., Bobick, A.F.: A multi-view method for gait recognition using static body parameters. In: Bigun, J., Smeraldi, F. (eds.) AVBPA 2001. LNCS, vol. 2091, pp. 301–311. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45344-X_44

    Chapter  Google Scholar 

  2. Jiang, C., Jiang, X., Sun, T.: Video filtration for content security based on multimodal features. Inf. Secur. Communi. Privacy 3, 76–77 (2012). (in Chinese)

    Google Scholar 

  3. Feng, W., Gao, J., Bill, P.B., et al.: Wireless capsule endoscopy video classification using an unsupervised learning approach. J. Image Graph. 16(11), 2041–2046 (2011). (in Chinese)

    Google Scholar 

  4. Fischer, S., Lienhart, R., Effelsberg, W.: Automatic recognition of film genres. In: Proceedings of the 3rd ACM International Conference on Multimedia, pp. 295–304. ACM Press, New York (1995)

    Google Scholar 

  5. Huang, C.N., Fu, T.J., Chen, H.C.: Text-based video content classification for online video-sharing sites. J. Am. Soc. Inf. Sci. Technol. 61(5), 891–906 (2010)

    Article  Google Scholar 

  6. Jiang, X.H., Sun, T.F., Wang, S.L.: An automatic video content classification scheme based on combined visual features model with modified DAGSVM. Multimedia Tools Appl. 52(1), 105–120 (2011)

    Article  Google Scholar 

  7. Subashini, K., Palanivel, S., Ramalingam, V.: Audio-video based classification using SVM. IUP J. Sci. Technol. 7(1), 44–53 (2011)

    Google Scholar 

  8. Lowe, D.G.: Distinctive image features from scaleinvariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  9. Bay, H., Tuytelaars, T., Van Gool, L.: SURF: speeded up robust features. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3951, pp. 404–417. Springer, Heidelberg (2006). https://doi.org/10.1007/11744023_32

    Chapter  Google Scholar 

  10. Zhen, E., Lin, J.: Unordered image key frame extraction based on image quality constraint. Comput. Eng. 43(11), 210–215 (2017)

    Google Scholar 

  11. Wang, Y., Sun, S., Ding, X.: A self-adaptive weighted affinity propagation clustering for key frames extraction on human action recognition. J. Vis. Commun. Image Representation 33(3), 193–202 (2015)

    Article  Google Scholar 

  12. Yi, R., Tomasi, C., Guibas, L.J.: Mover’s distance as a metric for image retrieval. Int. J. Comput. Vis. 40(2), 99–121 (2000)

    Article  MATH  Google Scholar 

  13. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Image Understand. 18(6), 679–698 (1986)

    Article  Google Scholar 

  14. Wang, X., Liu, X., Guan, Y.: Image edge detection algorithm based on improved Canny operator. Comput. Eng. 34(14), 196–198 (2012)

    Google Scholar 

  15. Wu, L., Yadong, C.Y.: Gesture recognition based on geometric features. Comput. Eng. Des. 35(2), 636–640 (2014). (in Chinese)

    Google Scholar 

  16. Binjue, Zhang, Liaoyin, Zhao, Yixuan, Wang: Fingeritip detection and gesture recognition based on kinect depth data. IEEE Trans. Comput. Sci. Technol. 3(1), 9–14 (2014)

    Google Scholar 

  17. Mitra, S., Acharya, T.: Gesture recognition: a servey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 37(3), 311–324 (2007)

    Article  Google Scholar 

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Acknowledgement

This work was supported by Surface Project of Natural Science Research in Colleges and Universities of Jiangsu China (No.16KJB520029), The Major Programs of Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 19KJA310002.) and The Natural Science Foundation of the Jiangsu Higher Education Institutions of China (No. 17KJD520006).

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Authors and Affiliations

  1. Nanjing Normal University of Special Education, Nanjing, 210038, China

    Zhaosong Zhu, Xianwei Jiang & Juxiao Zhang

  2. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Xianwei Jiang

Authors
  1. Zhaosong Zhu
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  2. Xianwei Jiang
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  3. Juxiao Zhang
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Corresponding author

Correspondence to Juxiao Zhang .

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Editors and Affiliations

  1. School of Informatics, University of Leicester, Leicestershire, UK

    Yu-Dong Zhang

  2. University of Leicester, Leicestershire, UK

    Shui-Hua Wang

  3. Human Normal University, Changsha, China

    Shuai Liu

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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Zhu, Z., Jiang, X., Zhang, J. (2020). Sign Language Video Classification Based on Image Recognition of Specified Key Frames. In: Zhang, YD., Wang, SH., Liu, S. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 327. Springer, Cham. https://doi.org/10.1007/978-3-030-51103-6_33

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  • DOI: https://doi.org/10.1007/978-3-030-51103-6_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-51102-9

  • Online ISBN: 978-3-030-51103-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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