DREAM: Dynamic RetriEval, Analysis and semantic metadata Management (DREAM)

梦想将开发有效的存储，管理，索引，搜索，使用和再利用数字媒体资产的后期制作行业。研发的重点是创建和使用从低级内容分析和高级语义感知生成的元数据，并将语义元数据纳入工作流程。一个新的元数据和语义干预将支持数据分析，索引和检索。DREAM将在中期创建媒体行业产品，并在安全，医疗，企业和消费者等其他领域实现后续应用程序的开发。该项目由屡获殊荣的英国后期制作技术开发商FilmLight领导，与一家专门从事媒体软件插件的公司（The Foundry）、一家主要用户合作伙伴（Double Negative）和一所专门从事语义技术的大学合作伙伴（阅读大学IMSS）合作。有三个技术层面。第一个将定义语义基础设施，包括元数据设计和领域本体，并开发用于语义视频索引、查询公式化和检索的工具。在这一点上，我们将解决“语义差距”相结合的元数据提取的图像处理与语义描述来自网络的本体，行为模型和交互式技术，识别用户的行为和上下文。在从低级过程的自动检测给出的线索不足的情况下，图像处理的迭代循环可以与特征提取和语义标记交错，直到有解决方案。第二部分将开发新的算法和工具，以基于图像处理和运动分析提取非常丰富的持久元数据。图像分析将与语义知识相联系，以提高元数据提取的准确性，而所产生的元数据反过来又将提高信息检索的对象识别能力（例如，通过制作比以前更准确的人体地图）。第三部分涉及数据库级别和数据管理，在元数据和数据在系统中移动时将它们关联起来。在基于图像的数据世界中，例如数字电影，对象太大且数量太多而无法保存在数据库中。DREAM将创建一个元数据库来跟踪与任何类型的分布式文件系统中保存的文件相关联的元数据。元数据可以是物理的和虚拟的，因为一些元数据类型（运动矢量分析和衍生物）是可以存储在文件系统本身中的大文件。由此产生的系统可以基于单个站点或通过安全VPN连接的多个站点。该项目将产生新的科学和技术知识，这些知识构成中期媒体行业一系列产品的基础，并为进一步的研发奠定基础，从而将其长期应用于其他部门。主要交付成果将是：- 后期制作过程的本体和语义模型，与流派相关的领域特定模型（例如动作、戏剧）和出现在其中的对象-用于标记和查询支持的语义解析引擎-用于检测、标记和转码数据的智能代理启用工具-用于半自动语义标记的模板和角色特定接口，查询和检索.从运动图像的低级特征中提取元数据的算法，具有到高级特征的语义链接. OFX插件标准的元数据格式定义和接口.用于运动估计、对象和字符遮片的新工具和插件，基于持久元数据和语义分析的使用的特征提取和修改-链接到用于非常大的分布式文件系统的语义使能的数据管理应用的元数据库-结合语义检索工具的集成原型解决方案，图像分析和元数据提取、元数据库和数据管理系统

DREAM will develop efficient storage, management, indexing, search, use and re-use of digital media assets for the postproduction industry. The R&D focuses on the creation and use of metadata generated from low-level content analysis and high-level semantic awareness, and the incorporation of semantic metadata into the workflow process. A novel Metadatabase and semantic intervention will support data analysis, indexing and retrieval. DREAM will create media industry products in the mid-term, and enable the subsequent development of applications in other sectors including security, medical, corporate, and consumer. The project is led by award-winning UK postproduction technology developer FilmLight, working with a company specialising in media software plug-ins (The Foundry), a major user partner (Double Negative) and a university partner (University of Reading IMSS) specialising in semantic technologies. There are three technical strands. The first will define the semantic infrastructure, involving metadata design and domain ontologies and develop tools for semantic video indexing, query formulation and retrieval. In this we will address the 'semantic gap' by combining metadata extracted by image processing with semantic descriptions derived from a network of ontologies, behavioural models and interactive techniques that recognise user behaviour and context. Where automatic detection from low-level processes gives insufficient clues, iterative cycles of image processing may be interleaved with feature extraction and semantic labelling until there is resolution. The second strand will develop new algorithms and tools to extract very rich, persistent metadata based on image processing and motion analysis. The image analysis will be linked to semantic knowledge to improve the accuracy of metadata extraction, and the resulting metadata will in turn improve the object recognition for information retrieval (by for example producing much more accurate human maps than previously possible). The third strand addresses the database level and data management, associating the metadata and data as they move through the system. In an image-based data world, such as digital cinema, the objects are too large and numerous to hold in a database. DREAM will create a Metadatabase to track the metadata associated with files held in any kind of distributed file system. The Metadatabase may be both physical and virtual, since some of the metadata types (motion vector analysis and derivatives) are large files that may be stored in the file system itself. The resulting system may be based on a single site or on multiple sites connected by a secure VPN. The project will result in new scientific and technological knowledge that forms the basis for a range of media industry products in the mid-term, and for further R&D leading to their long-term application to other sectors. The principal deliverables will be: - Ontologies and semantic models of postproduction processes, domain specific models relating to genres (such as action, drama) and objects appearing in them - A semantic resolution engine for labelling and query support - Smart proxy enabled tools for detection, labelling and transcoding data - Templates and role-specific interfaces for semi-automatic semantic labelling, query and retrieval - Algorithms for metadata extraction from low-level features of moving images, with semantic links to higher-level features - Metadata format definitions and interface for the OFX plug-in standard - New tools and plug-ins for motion estimation, object and character matting, feature extraction and modification based on the use of persistent metadata and semantic analysis - A metadatabase linked to a semantically enabled data management application for very large distributed file systems - An integrated prototype solution combining semantic retrieval tools, image analysis and metadata extraction, metadatabase and data management system