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A Framework for Hierarchical Big Image Data

机译:分层大图像数据的框架

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摘要

This framework displays a convincing getting ready framework assigned ICP (Image Cloud Processing) to effectively adjusting to the data impact in picture preparing field. While most past specialists focus on propelling the picture handling calculations to increment more prominent capability, this work centres around giving a general structure to those calculations that can be executed in parallel keeping in mind the end goal to accomplish the objective. The ICP framework contains two components, i.e. SICP (Static ICP) and DICP (Dynamic ICP). Specifically, SICP is away to handle the huge picture data pre-secured from the scattered system and DICP is proposed for dynamic data. To complete SICP, two data depictions, for example, P-Image and Big-Image are planned to organize with Map Reduce to achieve further developed setup and more noteworthy benefit. DICP is executed by means of a parallel preparing procedure working with the ordinary taking care of strategy of the appropriated structure.
机译:该框架显示了一个令人信服的准备就绪框架,该框架分配了ICP(图像云处理),可以有效地适应图片准备领域中的数据影响。尽管过去的大多数专家都专注于推动图片处理计算以增加更出色的功能,但这项工作的重点是为可以并行执行的那些计算提供一个总体结构,同时牢记实现目标的最终目标。 ICP框架包含两个组件,即SICP(静态ICP)和DICP(动态ICP)。具体而言,SICP不能处理从分散系统预先保护的海量图像数据,而DICP则用于动态数据。为了完成SICP,计划与Map Reduce一起组织两个数据描述,例如P-Image和Big-Image,以实现进一步开发的设置并带来更大的收益。 DICP是通过并行准备程序执行的,并且通常会考虑适当结构的策略。

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