Abstract
A large amount of useful information is included in the news video, and how to classify the news video information has become an important research topic in the field of multimedia technology. News videos are enormously informative, and employing manual classification methods is too time-consuming and vulnerable to subjective judgment. Therefore, developing an automated news video analysis and retrieval method becomes one of the most important research contents in the current multimedia information system. Therefore, this paper proposes a news video classification model based on ResNet-2 and transfer learning. First, a model-based transfer method was adopted to transfer the commonality knowledge of the pretrained model of the Inception-ResNet-v2 network on ImageNet, and a news video classification model was constructed. Then, a momentum update rule is introduced on the basis of the Adam algorithm, and an improved gradient descent method is proposed in order to obtain an optimal solution of the local minima of the function in the learning process. The experimental results show that the improved Adam algorithm can iteratively update the network weights through the adaptive learning rate to reach the fastest convergence. Compared with other convolutional neural network models, the modified Inception-ResNet-v2 network model achieves 91.47% classification accuracy for common news video datasets.
Highlights
Today, video media plays an increasingly prominent role in enriching people’s lives, education, and entertainment
With the continuous breakthroughs in typical network structures, deep neural networks such as recurrent neural network (RNN) [15], deep belief network (DBN) [16], and generative adversarial networks (GAN) [17] have emerged, which can better enhance the feature extraction ability of models by supervised learning [18, 19]. erefore, based on the theory and technology of content-based video retrieval, this paper focuses on the related technology and implementation of news video retrieval based on deep learning
Erefore, this paper adopts the idea of the transfer learning method and regards the Inception-residuals network (ResNet)-v2 network model after being pretrained by ImageNet large training set as a general image feature extractor
Summary
Video media plays an increasingly prominent role in enriching people’s lives, education, and entertainment. News is a kind of video, which is an important way for people to understand the society and is closely related to people’s life. Content-based retrieval refers to retrieval according to the semantic features or audio-visual features of media objects [6,7,8]. Semantic features refer to the content information of video segments, while audio-visual features refer to some physical features that can be directly obtained from sounds and images, such as colors, textures, and shapes in images, motions of objects and lenses in videos, and tonal loudness and timbre in sounds [9,10,11,12]. Erefore, based on the theory and technology of content-based video retrieval, this paper focuses on the related technology and implementation of news video retrieval based on deep learning With the continuous breakthroughs in typical network structures, deep neural networks such as recurrent neural network (RNN) [15], deep belief network (DBN) [16], and generative adversarial networks (GAN) [17] have emerged, which can better enhance the feature extraction ability of models by supervised learning [18, 19]. erefore, based on the theory and technology of content-based video retrieval, this paper focuses on the related technology and implementation of news video retrieval based on deep learning
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