DC: Small: Semantic Analysis of Large Multimedia Data Sets

这项研究致力于自动分析大量视频集合和连续处理的多个视频流的互动性和可扩展性。这项工作正在开发能够使用智能、主动处理集群和方法对用户定义的概念进行实时交互式视频搜索的机制，以及用于在分布式计算资源上从大量弱标记视频执行高精度语义视频分析的方法。该方法利用现代集群文件系统，其中数据存储在计算服务器的本地磁盘上，并且数据的位置对运行时系统可用，以允许计算和存储的协同定位。具体的研究目标是允许通过用于并行流处理的运行时来共同定位计算和存储，该并行流处理跨多核计算节点的集群并行化数据处理和机器学习任务。该项目还扩展了图形模型算法的分布式版本，以加快基本低级别信号处理步骤的计算和基于当前网络上可用的弱标记视频数据的语义分析。主要成果是演示了将并行实时视频流极大地加速、完整地处理到具有即时搜索能力的检索数据库中，并在交互搜索期间访问集群资源。这项工作的目标是开发由高速率流数据的实时处理驱动的交互式应用的原则。在这项工作中开发的处理体系结构和模块将使计算机视觉和多媒体开发人员能够在这个框架内有效地应用和测试他们自己的方法。

This research addresses interactivity and scalability in automatically analyzing large collections of video and multiple video streams processed continuously. This work is developing mechanisms to enable real-time interactive video search for user defined concepts using intelligent, active processing clusters and methods for performing high-accuracy semantic video analysis from large amounts of weakly-labeled video over distributed computing resources. The methods leverage modern cluster file systems where data is stored on the local disks of the compute servers, and the location of data is made available to the runtime system to allow co-location of compution and storage.The specific research objectives are to allow co-location of compute and storage through a runtime for parallel stream processing that parallelizes data processing and machine learning tasks across a cluster of multi-core compute nodes. The project also extends distributed versions of graphic model algorithms to speed computation of both the basic low-level signal processing steps and for the semantic analysis based on weakly labeled video data as currently available on the web. The main outcome is to demonstrate vastly accelerated, complete processing of parallel live video streams into a retrieval database with immediate search capabilities and accessing cluster resources during interactive search. The goal of this work is to develop principles for interactive applications driven by real-time processing of high-rate streaming data. The processing architecture and modules developed in this work will enable computer vision and multimedia developers to efficiently apply and test their own methods within this framework.