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Content Based Video Retrieval by Using Distributed Real-Time System Based on Storm

机译:基于暴风雨的分布式实时系统基于内容的视频检索

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摘要

Time processing is a challenging issue for content-based video retrieval systems, especially when the process of indexing, classifying and retrieving desired and relevant videos is from a huge database. A CBVR system called bounded coordinate of motion histogram (BCMH) has been implemented as a case study. The BCMH offline step requires a long time to complete the learning phase, and the online step falls short in addressing the real-time video processing. To overcome these drawbacks, this article presents a batch-oriented computing based on Apache Hadoop to improve the time processing for the offline step, and a real-time oriented computing based on Apache Storm topologies to achieve a real-time response for the online step. The proposed approach is tested on the HOLLYWOOD2 dataset and the obtained results demonstrate reliability and efficiency of the proposed method.
机译:对于基于内容的视频检索系统来说,时间处理是一个具有挑战性的问题,尤其是当索引,分类和检索所需视频以及相关视频的过程来自一个庞大的数据库时。作为案例研究,已实现了一种称为运动直方图有界坐标(BCMH)的CBVR系统。 BCMH离线步骤需要很长时间才能完成学习阶段,而在线步骤不足以解决实时视频处理问题。为了克服这些缺点,本文提出了一种基于Apache Hadoop的面向批处理的计算,以改进脱机步骤的时间处理,以及一种基于Apache Storm拓扑的面向实时的计算,以实现对联机步骤的实时响应。 。该方法在HOLLYWOOD2数据集上进行了测试,所得结果证明了该方法的可靠性和有效性。

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