.png){kind=icon}
返回
AI-driven video summarization for optimizing content retrieval and management through deep learning techniques
.png){kind=icon}
OA
AI
DOI：10.1038/s41598-025-87824-9.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
With the rapid advancement of artificial intelligence, questions are increasingly being raised by stakeholders regarding how such technologies can enhance the environmental, social, and governance outcomes of organizations. In this study, challenges related to the organization and retrieval of video content within large, heterogeneous media archives are addressed. Existing methods, often reliant on human intervention or low-complexity algorithms, are observed to struggle with the growing demands of online video quantity and quality. To address these limitations, a novel approach is proposed, where convolutional neural networks and long short-term memory networks are utilized to extract both frame-level and temporal video features. Residual networks 50 (ResNet50) is integrated for enhanced content representation, and two-frame video flow is employed to improve system performance. The framework achieves precision, recall, and F-score of 79.2%, 86.5%, and 83%, respectively, on the YouTube, EPFL, and TVSum datasets. Beyond technological advancements, opportunities for effective content management are highlighted, emphasizing the promotion of sustainable digital practices. By minimizing data duplication and optimizing resource usage, scalable solutions for large media collections are supported by the proposed system.
Keyword:
Video summarization
Content retrieval
Convolutional neural networks
LSTM
ResNet50
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
3.9
论文数:
27.8W
被引数:
83.5W
机构
引用论文
分享收藏Spatial-Spectral Feature Extraction via Deep ConvLSTM Neural Networks for Hyperspectral Image Classification基于Deep ConvLSTM神经网络的空间光谱特征提取及高光谱图像分类OAAI
OAAI分享收藏Two-Level Attention Module Based on Spurious-3D Residual Networks for Human Action Recognition.png)
SENSORSOAAI
SENSORSIF3.5
OAAI分享收藏
查看更多内容