Scalable Cross-Media Hashing for Searching Heterogeneous Data Sources

用于搜索异构数据源的可扩展跨媒体哈希

• 批准号: DP160104058
• 负责人: Prof Heng Tao Shen
• 金额: $ 7.99万
• 依托单位: The University of Queensland
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2016
• 资助国家: 澳大利亚
• 起止时间: 2016-01-01 至 2017-08-21
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP160104058
• 关键词: Scalable; Cross; Media; Hashing; Searching

This project aims to use novel indexing and search approaches to realise the value of multimedia data. The ever-increasing availability and heterogeneity of multimedia data are calling for new approaches to search information across different media types and data sources. This project aims to enable accurate and real-time cross-media searches from billions of multimedia objects collected from various media sources by tackling the heterogeneity and scalability issues. Expected outcomes include scalable cross-media hashing techniques to capture implicit correlations existing in heterogeneous data and embed high-dimensional features into short binary codes; new binary code indexing and ranking schemes to further improve search speed and quality; and a large-scale cross-media system to evaluate methods and demonstrate the practical value.

这个项目旨在使用新的索引和搜索方法来实现多媒体数据的价值。随着多媒体数据的可用性和异构性的不断提高，人们需要新的方法来搜索跨媒体类型和数据源的信息。该项目旨在通过解决异构性和可扩展性问题，实现从各种媒体源收集的数十亿多媒体对象的准确和实时跨媒体搜索。预期成果包括可扩展的跨媒体哈希技术，以捕获异构数据中存在的隐含相关性，并将高维特征嵌入到短二进制代码中;新的二进制代码索引和排名方案，进一步提高搜索速度和质量;和一个大规模的跨媒体系统来评估方法并展示实用价值。