DC: Small: Semantic Analysis of Large Multimedia Data Sets

DC：小型：大型多媒体数据集的语义分析

• 批准号: 0917072
• 负责人: Alexander Hauptmann
• 金额: $ 45万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-01 至 2013-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0917072&HistoricalAwards=false
• 关键词: DC; Small; Semantic; Analysis; Large; DC; Small; Semantic; Analysis; Large

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.

这项研究解决了自动分析大量视频集和多个视频流的互动性和可扩展性。这项工作正在开发机制，以使用智能的，主动的处理簇和方法对用户定义的概念进行实时交互式视频搜索，从而从分布式计算资源上从大量弱标记的视频中执行高准确的语义视频分析。该方法利用现代集群文件系统，将数据存储在计算服务器的本地磁盘上，并且可以向运行时系统提供数据位置，以允许共同置换构成和存储。该特定的研究目标是允许通过运行时进行计算和存储，以便于并行流流处理，以使数据处理和机器学习任务与多级计算的compuster comptuse comptuse node computs compustus comptuse node computs compute node node node computy node node node node node node node。该项目还将图形模型算法的分布式版本扩展到了基本的低级信号处理步骤和基于网络上当前可用的弱标记的视频数据的语义分析的速度计算。主要结果是演示将并行实时视频流的完整加速，完整的处理到具有即时搜索功能的检索数据库中，并在交互式搜索过程中访问群集资源。这项工作的目的是为由高速流数据的实时处理驱动的交互式应用程序制定原理。这项工作中开发的处理结构和模块将使计算机视觉和多媒体开发人员在此框架内有效地应用和测试自己的方法。