Abstract
In order to improve that segmentation quality of the video image of the marathon, a video image segmentation algorithm based on machine learning is proposed. Constructing the edge contour feature detection and the pixel feature point fusion reconstruction model of the marathon moving video image, carrying out multi-level feature decomposition and gray pixel feature separation of the marathon moving video image, and establishing a visual feature reconstruction model of the marathon moving video image, the feature segmentation and the edge contour feature detection of the marathon moving video image are carried out in combination with the block area template matching method, the similarity information fusion model is used for carrying out the video information fusion awareness and the block area template matching in the process of the marathon moving video image segmentation, the fuzzy feature quantity of the moving video image of the marathon is extracted, and the machine learning method is adopted to realize the fusion awareness and the segmentation quality evaluation of the marathon moving video information. The simulation results show that the method is good in image segmentation quality and high in image recognition, and the output signal-to-noise ratio of the motion feature reconstruction of the marathons moving video is high.
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Mukaino, M., Ono, T., Shindo, K., et al.: Efficacy of brain-computer interface-driven neuromuscular electrical stimulation for chronic paresis after stroke. J. Rehab. Med. 46(4), 378–382 (2014)
Moghaddam, Z., Piccardi, M.: Training initialization of hidden Markov models in human action recognition. IEEE Trans. Autom. Sci. Eng. 11(2), 394–408 (2014)
Amor, B.B., Su, J., Srivastava, A.: Action recognition using rate-invariant analysis, of skeletal shape trajectories. IEEE Trans. Pattern Anal. Mach. Intell. 38(1), 1–13 (2016)
Songlin, L., Yong, J., Yong, G., Xiaoling, Z., Guolong, C.: Moving target tracking algorithm based on improved Camshift for through-wall-radar imaging. J. Comput. Appl. 38(2), 528–532 (2018)
Jie-yu, Z., Hong-ping, Z., Shu, C.: Face recognition based on weighted local binary pattern with adaptive threshold. J. Electron. Inform. Technol. 36(6), 1327–1333 (2014)
Naseem, I., Togneri, R., Bennamoun, M.: Linear regression for face recognition. IEEE Trans. Pattern Anal. Mach. Intell. 32(11), 2106–2112 (2010)
Wagner, A., Wright, J., Ganesh, A., Zhou, Z., Mobahi, H., Ma, Y.: Toward a practical face recognition system: robust alignment and illumination by sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 372–386 (2012)
Wu, J.G., Shao, T., Liu, Z.Y.: RGB-D saliency detection based on integration feature of color and depth saliency map. J. Electron. Inform. Technol. 39(9), 2148–2154 (2017)
Carlson, N.A., Porter, J.R.: On the cardinality of Hausdorff spaces and H-closed spaces. Topology Appl. 160(1), 137–142 (2017)
Yue, X., Mengru, F., Jiatian, P., Yong, C.: Remote sensing image segmentation method based on deep learning model. J. Compu. Appl. 39(10), 2905–2914 (2019)
Gao, H.Y., Wu, B.: Object-oriented classification of high spatial resolution remote sensing imagery based on image segmentation with pixel shape feature. Remote Sens. Inf. 6, 67–72 (2010)
Yuan, J., Wang, D., Li, R.: Remote sensing image segmentation by combining spectral and texture features. IEEE Trans. Geosci. Remote Sens. 52(1), 16–24 (2014)
Krähenbvhl, P., Koltun, V.: Efficient inference in fully connected CRFs with Gaussian edge potentials. In: Proceedings of the 2011 International Conference on Neural Information Processing Systems, pp. 109–117. Curran Associates Inc., New York (2011)
Lan, G., Zihan, G., Yao, W.: Gastric tumor cell image recognition method based on radial transformation and improved AlexNet. J. Comput. Appl. 39(10), 2923–2929 (2019)
Huang, B., Zhao, B., Song, Y.: Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery. Remote Sens. Environ. 214, 73–86 (2018)
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© 2020 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Qiang, H., Yi-de, L. (2020). Image Segmentation Technology of Marathon Motion Video Based on Machine Learning. 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 326. Springer, Cham. https://doi.org/10.1007/978-3-030-51100-5_19
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DOI: https://doi.org/10.1007/978-3-030-51100-5_19
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