Collaborative: Compressed Domain Search for Text and Images by Sorted Contexts

协作：按排序上下文对文本和图像进行压缩域搜索

• 批准号: 0207819
• 负责人: Amar Mukherjee
• 金额: $ 9.15万
• 依托单位: The University of Central Florida Board of Trustees
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-08-15 至 2005-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0207819&HistoricalAwards=false
• 关键词: Collaborative; Compressed; Domain; Search; Text

With the dramatic increase in the amount of multimedia data, efficiencyconsiderations make it important to consider ways to keep the data in the compressed form for as much as possible, even when it is searched. We propose to develop methodologies for searching directly on compressed text and images, when the compression is based on the family of compression algorithms that depend on sorted context. We also propose to develop search-aware data compression schemes for text and images based on sorted context that will support compressed-domain search directly on the compressed data.For text, we will explore the applicability of the classical linear exact pattern matching algorithms and binary search algorithms for textcompressed with the Burrows-Wheeler transform. We will develop similarcompression schemes for images based on sorted image contexts, calledLICSIC (Lossless Image Compression by Sorted Image Contexts).The results of the proposed research will be important in environmentsthat require the organization and management of huge collections of textor images. Internet search engines, digital libraries, satellite and space data , geographical information systems, bio-informatics and medical diagnostics are typical candidate applications.

随着多媒体数据量的急剧增加，效率征服的效率使考虑将数据尽可能多地以压缩形式保存的方法也很重要，即使搜索也是如此。当压缩基于依赖于排序上下文的压缩算法家族时，我们建议开发直接在压缩文本和图像上搜索的方法。我们还建议基于排序上下文开发文本和图像的搜索感知数据压缩方案，该方案将直接支持压缩数据的压缩域搜索。对于文本，我们将探索经典线性精确模式匹配算法和二进制搜索算法的适用性，以使burrows-wheeler变换具有文本comperted。我们将根据排序的图像上下文（称为limicsic（通过排序图像上下文上下文的无损图像压缩）为图像制定相似的压制方案。拟议的研究的结果在环境中将很重要，这需要组织和管理大量托管图像的集合。互联网搜索引擎，数字库，卫星和空间数据，地理信息系统，生物信息学和医学诊断是典型的候选应用程序。