Research Article
Video Knowledge Discovery Based on Convolutional Neural Network
@INPROCEEDINGS{10.1007/978-3-030-48513-9_28, author={JinJiao Lin and ChunFang Liu and LiZhen Cui and WeiYuan Huang and Rui Song and YanZe Zhao}, title={Video Knowledge Discovery Based on Convolutional Neural Network}, proceedings={Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications. 9th EAI International Conference, CloudComp 2019, and 4th EAI International Conference, SmartGIFT 2019, Beijing, China, December 4-5, 2019, and December 21-22, 2019}, proceedings_a={CLOUDCOMP}, year={2020}, month={6}, keywords={Knowledge discovery TI-pooling Convolutional nerve}, doi={10.1007/978-3-030-48513-9_28} }- JinJiao Lin
ChunFang Liu
LiZhen Cui
WeiYuan Huang
Rui Song
YanZe Zhao
Year: 2020
Video Knowledge Discovery Based on Convolutional Neural Network
CLOUDCOMP
Springer
DOI: 10.1007/978-3-030-48513-9_28
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.