多模态数据库的融合技术研究
摘 要
随着信息技术的迅猛发展，多模态数据在各个领域中的应用日益广泛，如医疗影像与文本报告结合、智能交通中视频与传感器数据融合等场景对多模态数据库融合技术提出了迫切需求。本研究旨在探索一种高效可靠的多模态数据库融合方法，以解决不同类型数据之间的语义鸿沟和异构性问题。为此，提出了一种基于深度学习框架的多模态特征映射模型，通过构建跨模态关联矩阵实现不同模态数据间的语义对齐，并引入注意力机制增强关键信息表达能力。实验结果表明，在多个公开数据集上该方法能够显著提高检索精度和分类准确率，相较于传统方法平均提升15%以上。此外，所提出的融合策略具备良好的可扩展性和鲁棒性，能够在噪声环境下保持稳定性能。
关键词：多模态数据融合 深度学习框架 语义对齐
Abstract
With the rapid development of information technology, the application of multimodal data is increasingly wide in various fields, such as the combination of medical image and text report, the fusion of video and sensor data in intelligent transportation and other scenarios have put forward an urgent demand for multimodal database fusion technology. This study aims to explore an efficient and reliable multimodal database fusion method to address the semantic gap and heterogeneity between different types of data. To this end, a multimodal feature mapping model based on deep learning fr amework is proposed to realize semantic alignment between different modal data by constructing cross-modal correlation matrix, and introduce attention mechanism to enhance key information ex pression ability. The experimental results show that the proposed method can significantly improve the retrieval accuracy and classification accuracy on multiple public data sets, with an average improvement of more than 15% compared with the traditional methods. Moreover, the proposed fusion strategy has good scalability and robustness and can maintain stable performance in noisy environment.
Keyword:Multimodal Data Fusion Deep Learning fr amework Semantic Alignment
目 录
1绪论 1
1.1多模态数据库融合的背景与意义 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2多模态数据特征分析 2
2.1数据类型与特性 2
2.2模态间关联性研究 3
2.3特征提取与表示方法 3
3融合技术框架构建 4
3.1融合策略选择 4
3.2关键技术实现 4
3.3系统架构设计 5
4应用场景与效果评估 6
4.1典型应用场景分析 6
4.2性能测试与评价 6
4.3结果分析与讨论 7
结论 8
参考文献 9
致谢 10
摘 要
随着信息技术的迅猛发展，多模态数据在各个领域中的应用日益广泛，如医疗影像与文本报告结合、智能交通中视频与传感器数据融合等场景对多模态数据库融合技术提出了迫切需求。本研究旨在探索一种高效可靠的多模态数据库融合方法，以解决不同类型数据之间的语义鸿沟和异构性问题。为此，提出了一种基于深度学习框架的多模态特征映射模型，通过构建跨模态关联矩阵实现不同模态数据间的语义对齐，并引入注意力机制增强关键信息表达能力。实验结果表明，在多个公开数据集上该方法能够显著提高检索精度和分类准确率，相较于传统方法平均提升15%以上。此外，所提出的融合策略具备良好的可扩展性和鲁棒性，能够在噪声环境下保持稳定性能。
关键词：多模态数据融合 深度学习框架 语义对齐
Abstract
With the rapid development of information technology, the application of multimodal data is increasingly wide in various fields, such as the combination of medical image and text report, the fusion of video and sensor data in intelligent transportation and other scenarios have put forward an urgent demand for multimodal database fusion technology. This study aims to explore an efficient and reliable multimodal database fusion method to address the semantic gap and heterogeneity between different types of data. To this end, a multimodal feature mapping model based on deep learning fr amework is proposed to realize semantic alignment between different modal data by constructing cross-modal correlation matrix, and introduce attention mechanism to enhance key information ex pression ability. The experimental results show that the proposed method can significantly improve the retrieval accuracy and classification accuracy on multiple public data sets, with an average improvement of more than 15% compared with the traditional methods. Moreover, the proposed fusion strategy has good scalability and robustness and can maintain stable performance in noisy environment.
Keyword:Multimodal Data Fusion Deep Learning fr amework Semantic Alignment
目 录
1绪论 1
1.1多模态数据库融合的背景与意义 1
1.2国内外研究现状综述 1
1.3本文研究方法概述 1
2多模态数据特征分析 2
2.1数据类型与特性 2
2.2模态间关联性研究 3
2.3特征提取与表示方法 3
3融合技术框架构建 4
3.1融合策略选择 4
3.2关键技术实现 4
3.3系统架构设计 5
4应用场景与效果评估 6
4.1典型应用场景分析 6
4.2性能测试与评价 6
4.3结果分析与讨论 7
结论 8
参考文献 9
致谢 10
