Abstract
Under the background of Internet+education, video course resources are becoming more and more abundant, at the same time, the Internet has a large number of not named or named non-standard courses video. It is increasingly important to identify courses name in these abundant video course teaching resources to improve learner efficiency. This study utilizes a deep neural network framework that incorporates a simple to implement transformation-invariant pooling operator (TI-pooling), after the audio and image information in course video is processed by the convolution layer and pooling layer of the model, the TI-pooling operator will further extract the features, so as to extract the most important information of course video, and we will identify the course name from the extracted course video information. The experimental results show that the accuracy of course name recognition obtained by taking image and audio as the input of CNN model is higher than that obtained by only image, only audio and only image and audio without ti-pooling operation.
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Acknowledgement
This work was financially supported by the Teaching Reform Research Project of Undergraduate Colleges and Universities of Shandong Province (Z2016Z036), the Teaching Reform Research Project of Shandong University of Finance and Economics (jy2018062891470, jy201830, jy201810), Shandong Provincial Social Science Planning Research Project (18CHLJ08), Scientific Research Projects of Universities in Shandong Province (J18RA136).
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Lin, J., Liu, C., Cui, L., Huang, W., Song, R., Zhao, Y. (2020). Video Knowledge Discovery Based on Convolutional Neural Network. In: Zhang, X., Liu, G., Qiu, M., Xiang, W., Huang, T. (eds) Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. CloudComp SmartGift 2019 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-030-48513-9_28
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DOI: https://doi.org/10.1007/978-3-030-48513-9_28
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