News Video Classification Model Based on ResNet-2 and Transfer Learning

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.

A novel multi-modal feature extraction system for news video

“…Presently, the feature extraction and processing of videos are divided primarily into textual and audio-visual features. Textual features relate to the content information of video clips, while audio-visual features derive from the physical characteristics of sound and images [5]. Initially, video feature extraction primarily focused on extracting textual features.…”

Retracted: News Video Classification Model Based on ResNet-2 and Transfer Learning

“…Tis article has been retracted by Hindawi, as publisher, following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of systematic manipulation of the publication and peer-review process.…”