由人工智能驱动的视频总结,通过深度学习技术优化内容检索和管理
Deepali Vora1, Payal Kadam2,3, Dadaso D Mohite4
1Symbiosis Institute of Technology, Pune Campus, Symbiosis International (Deemed University), Pune, India.
Scientific reports
|February 3, 2025
概括
本研究介绍了一种人工智能驱动的系统,用于组织大型档案中的视频内容,提高检索效率并促进可持续的数字实践,以提高环境,社会和治理成果.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
- 信息科学 信息科学 信息科学
背景情况:
- 数字视频内容的数量和复杂性越来越大,挑战了传统的档案管理.
- 现有的视频组织和检索方法往往需要大量的人力投入或使用简单的算法.
- 需要可扩展和高效的解决方案来管理大型,异质的媒体档案.
研究的目的:
- 开发一种新的人工智能 (AI) 框架,以加强视频内容的组织和检索.
- 为了解决当前处理大规模视频档案方法的局限性.
- 探索AI如何支持媒体管理中的可持续数字实践.
主要方法:
- 利用卷积神经网络 (CNN) 和长短期记忆 (LSTM) 网络来提取级和时间视频特征.
- 综合剩余网络50 (ResNet50) 改进内容表示.
- 采用双视频流来提高系统性能.
主要成果:
- 实现了高性能指标:79.2%的精度,86.5%的回忆和83%的F-score.
- 在各种数据集上验证了框架,包括YouTube,EPFL和TVSum.
- 证明了系统能够最大限度地减少数据重复和优化资源使用的能力.
结论:
- 拟议的AI框架为管理大型媒体集合提供了可扩展和有效的解决方案.
- 该系统通过优化资源利用来促进可持续的数字实践.
- 通过改进的内容管理,人工智能的进步可以显著提高组织的环境,社会和治理 (ESG) 结果.
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