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基于视觉传递和多媒体数据融合的参数艺术创作平台设计
1School of International Exchange, Kyungil University, Gyeongsan-s, Gyeongsangbuk-do, Republic of South Korea.
PeerJ. Computer science
|September 24, 2025
概括
本研究介绍了一种双模时间序列分类模型 (BTSCM),用于对在线艺术品进行分类. BTSCM网络在视频分类中达到90%以上的准确性,改进了艺术内容分析.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 多媒体分析分析.
背景情况:
- 艺术交流越来越多地发生在虚拟平台上,需要有效的内容分类.
- 广泛的数字艺术收藏的手动标签是耗时和资源密集的.
- 现有的方法难以应对多模式艺术内容的复杂性.
研究的目的:
- 引入创新的双模时间序列分类模型 (BTSCM) 用于在虚拟平台上对艺术品进行分类和标签.
- 为高精度视频分类利用多媒体融合技术.
- 为数字艺术平台的自动化内容分析提供基础.
主要方法:
- BTSCM网络将视频数据分类为图像和声音元素.
- 功能提取使用膨胀的3D ConvNet用于视觉数据和Mel频率 cepstrum系数 (MFCC) 用于音频.
- 功能合成采用完全卷积网络 (FCN),深度Q网络 (DQN) 和长短期内存 (LSTM) 的融合.
主要成果:
- BTSCM框架在各种视频数据集中展示了出色的分类结果.
- 在自主建立的艺术平台上实现了超过90%的分类识别率.
- 性能优于现有的多式联通融合识别网络.
结论:
- BTSCM网络为艺术品的高精度视频分类提供了非常有效的解决方案.
- 该模型在数字艺术内容的自动审查和注释方面取得了重大进展.
- 这一框架为未来的艺术创作平台内容分析研究奠定了基础.
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