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MapReduce based Big Data Framework for Content Searching of Surveillance System Videos

机译:基于MapReduce的大数据框架,用于监视系统视频的内容搜索

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CBIR systems go through sets of stages starling from acquiring the new images, representing these images In-extracting the image features, describing the key features and eventually computing the similarity distances to get the most relevant results responding to the query image. In this paper, ICBIR an integrated CBIR Hadoop-MapReduee based framework which is split into both offline and online phases is introduced. Visual statements are built using the extracted interest points SIFTs. Later on. these visual statements are used to estimate the similarity distances which in turn are used to create the image dataset clusters. A huge vocabulary of SIFTs describing the interest points of the image is constructed. In this paper, the authors are interested in routing protocols based on clusters that aim to reduce congestion in order to have reliable data transmission and a reduced loss rate. This is achieved by balancing the traffic load, which results into a balanced energy consumption within the network.
机译:CBIR系统经历了从获取新图像到代表这些图像的一系列阶段,从图像中提取图像特征,描述关键特征并最终计算相似距离,以获得响应查询图像的最相关结果。在本文中,ICBIR介绍了一个基于CBIR Hadoop-MapReduee的集成框架,该框架分为离线阶段和在线阶段。使用提取的兴趣点SIFT构建可视化报表。稍后的。这些视觉陈述用于估计相似距离,而相似距离又用于创建图像数据集聚类。大量的SIFT词汇表描述了图像的兴趣点。在本文中,作者对基于群集的路由协议感兴趣,该协议旨在减少拥塞,以实现可靠的数据传输和降低的丢失率。这是通过平衡流量负载来实现的,从而导致网络内的能耗平衡。

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