利用Hadoop-MapReduce模型改进基于内容的视频检索性能

2018 23rd Conference of Open Innovations Association (FRUCT) Pub Date : 2018-11-01 DOI:10.23919/FRUCT.2018.8588095

El Mehdi Saoudi, Abderrahmane Adoui El Ouadrhiri, Othman El Warrak, Said Jai-Andaloussi, Abderrahmane Sekkaki

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引用次数: 3

摘要

本文提出了一种基于MapReduce编程模型的分布式基于内容的视频检索(CBVR)系统。本文以一个基于Hadoop框架的运动直方图有界坐标(bounded Coordinate of Motion Histogram, BCMH)的CBVR系统为例进行了研究。为了减少训练过程的计算时间，我们提出了一种基于分配给Hadoop集群多个节点的Mapreduce任务集的分布式模型来提取视频签名并计算与BCMH系统的相似度。在HOLLYWOOD2数据集上进行了测试，结果证明了该方法的有效性。

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Improving Content Based Video Retrieval Performance by Using Hadoop-MapReduce Model

In this paper, we present a distributed Content-Based Video Retrieval (CBVR) system based on MapReduce programming model. A CBVR system called bounded Coordinate of Motion Histogram (BCMH) has been implemented as case study by using Hadoop framework. Our work consists of proposing a distributed model to extract videos signatures and compute similarity with the BCMH system based on a set of Mapreduce jobs assigned to multiple nodes of the Hadoop cluster in order to reduce computation time of training process. The proposed approach is tested on HOLLYWOOD2 dataset and the obtained results demonstrate efficiency of the proposed approach.

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2018 23rd Conference of Open Innovations Association (FRUCT)

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