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CSISE: Cloud-based Semantic Image Search Engine.

机译:CSISE:基于云的语义图像搜索引擎。

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摘要

Due to rapid exponential growth in data, a couple of challenges we face today are how to handle big data and analyze large data sets. An IBM study showed the amount of data created in the last two years alone is 90% of the data in the world today. We have especially seen the exponential growth of images on the Web, e.g., more than 6 billion in Flickr, 1.5 billion in Google image engine, and more than 1 billon images in Instagram. Since big data are not only a matter of a size, but are also heterogeneous types and sources of data, image searching with big data may not be scalable in practical settings. We envision Cloud computing as a new way to transform the big data challenge into a great opportunity.;In this thesis, we intend to perform an efficient and accurate classification of a large collection of images using Cloud computing, which in turn supports semantic image searching. A novel approach with enhanced accuracy has been proposed to utilize semantic technology to classify images by analyzing both metadata and image data types. A two-level classification model was designed (i) semantic classification was performed on a metadata of images using TF-IDF, and (ii) image classification was performed using a hybrid image processing model combined with Euclidean distance and SURF FLANN measurements.;A Cloud-based Semantic Image Search Engine (CSISE) is also developed to search an image using the proposed semantic model with the dynamic image repository by connecting online image search engines that include Google Image Search, Flickr, and Picasa. A series of experiments have been performed in a large-scale Hadoop environment using IBM's cloud on over half a million logo images of 76 types. The experimental results show that the performance of the CSISE engine (based on the proposed method) is comparable to the popular online image search engines as well as accurate with a higher rate (average precision of 71%) than existing approaches.
机译:由于数据呈指数级增长,我们今天面临的两个挑战是如何处理大数据和分析大数据集。 IBM的一项研究表明,仅在过去两年中创建的数据量就占当今世界数据的90%。我们特别看到了网络上图像的指数增长,例如Flickr超过60亿,Google图像引擎超过15亿,Instagram上超过1 Billion图像。由于大数据不仅是大小的问题,而且还是异构类型和数据源,因此在实际环境中使用大数据进行图像搜索可能无法扩展。我们设想将云计算作为一种将大数据挑战转化为巨大机遇的新方法。;本文旨在使用云计算对大量图像进行高效而准确的分类,从而支持语义图像搜索。已经提出了一种具有更高准确性的新颖方法,该方法利用语义技术通过分析元数据和图像数据类型对图像进行分类。设计了一个两级分类模型(i)使用TF-IDF对图像的元数据执行语义分类,以及(ii)使用结合了欧氏距离和SURF FLANN测量的混合图像处理模型进行图像分类。通过连接包括Google Image Search,Flickr和Picasa在内的在线图像搜索引擎,还开发了基于云的语义图像搜索引擎(CSISE)以使用提议的语义模型和动态图像存储库搜索图像。使用IBM的云,在大规模的Hadoop环境中,对76种类型的超过50万个徽标图像进行了一系列实验。实验结果表明,CSISE引擎(基于所提出的方法)的性能可与流行的在线图像搜索引擎相媲美,并且具有比现有方法更高的准确率(平均精度为71%)。

著录项

  • 作者

    Walunj, Vijay.;

  • 作者单位

    University of Missouri - Kansas City.;

  • 授予单位 University of Missouri - Kansas City.;
  • 学科 Computer Science.;Web Studies.
  • 学位 M.S.
  • 年度 2014
  • 页码 69 p.
  • 总页数 69
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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