Using Grid technology for commercial scale content based image retrieval

使用网格技术进行商业规模的基于内容的图像检索

• 批准号: ST/F003625/1
• 负责人: Michael Andrew Parker
• 金额: $ 9.14万
• 依托单位: University of Cambridge
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2007
• 资助国家: 英国
• 起止时间: 2007 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=ST%2FF003625%2F1
• 关键词: Using; Grid; technology; commercial; scale

Professor Parker's group holds a tremendous amount of expertise on developing and running Grid computing resources. Imense Ltd needs to be able to process hundreds of millions of images in a reliable, scaleable and efficient manner. Imense Ltd has already identified Grid computing as an important enabling technology for its future image search products but does not however have the resources or the time to adopt it without external support. Imense Ltd would like to deploy improved versions of gLite and Ganga on its own infrastructure (we already have a modest 120 CPU linux cluster) with the possibility to expand its Grid using virtual servers from third party providers. This will form the processing backbone for our goal of becoming the dominant image search portal on the web. Scalability is key as there are now numerous photo-hosting websites each holding hundreds of millions of images (www.flickr.com holds over 400m images as do www.webshots.com and www.photobucket.com). We would also like to be in the position of using whatever is the 'standard' for enterprise Grid management. The proposed knowledge to be transferred is: 1) How to set up and manage a scaleable Grid of computers. 2) How to overcome data bottlenecks. 3) How to interact with other Grids 4) What uses can the upcoming field of virtualisation be put to in grid computing. Imense Ltd has collaborated with Professor Parker and Dr Karl Harrison in a previous miniPIPPS project (PP/E5089581/1) and found them to be ideal partners. They demonstrated an impressive ability to identify and vercome (or circumvent) any obstacle to the use of Grid computing for large scale image indexing tasks. Through the previous collaboration we were able to extend the size of our processed test corpus from roughly 50,000 images to 3,000,000 images. We feel confident that a further collaboration would allow Imense Ltd to extend this by a further two orders of magnitude which will make our image search solution commercially viable.

帕克教授的小组在开发和运行网格计算资源方面拥有大量的专业知识。Imense Ltd需要能够以可靠、可扩展和高效的方式处理数亿张图像。Imense Ltd已经将网格计算确定为其未来图像搜索产品的重要支持技术，但是没有资源或时间在没有外部支持的情况下采用它。Imense有限公司希望在自己的基础设施上部署gLite和Ganga的改进版本（我们已经有一个适度的120 CPU Linux集群），并有可能使用第三方提供商的虚拟服务器扩展其网格。这将成为我们成为网络上占主导地位的图像搜索门户的目标的处理骨干。可扩展性是关键，因为现在有许多照片托管网站，每个网站都拥有数亿张图片（www.flickr.com拥有超过4亿张图片，www.example.com和www.photobucket.com也是如此）。www.webshots.com我们还希望能够使用任何企业网格管理的“标准”。建议要转移的知识是：1）如何建立和管理一个可扩展的计算机网格。2)如何克服数据瓶颈3)如何与其他网格交互4）即将到来的虚拟化领域在网格计算中有什么用途。Imense有限公司曾与帕克教授和卡尔·哈里森博士在以前的miniPIPPS项目（PP/E5089581/1），并发现他们是理想的合作伙伴。他们展示了一个令人印象深刻的能力，以确定和克服（或规避）任何障碍，使用网格计算的大规模图像索引任务。通过之前的合作，我们能够将处理后的测试语料库的大小从大约50，000张图像扩展到3，000，000张图像。我们相信，进一步的合作将使Imense有限公司将其进一步扩展两个数量级，这将使我们的图像搜索解决方案在商业上可行。