Intensive Use of Correspondence Analysis for Large Scale Content-Based Image Retrieval

机译：大量使用对应分析进行基于内容的大规模图像检索

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摘要

In this paper, we investigate the intensive use of Correspondence Analysis (CA) for large scale content-based image retrieval. Correspondence Analysis is a useful method for analyzing textual data and we adapt it to images using the SIFT local descriptors. CA is used to reduce dimensions and to limit the number of images to be considered during the search step. An incremental algorithm for CA is proposed to deal with large databases giving exactly the same result as the standard algorithm. We also integrate the Contextual Dissimilarity Measure in our search scheme in order to improve response time and accuracy. We explore this integration in two ways: (i) off-line (the structure of image neighborhoods is corrected off-line) and (ii) on-the-fly (the structure of image neighborhoods is adapted during the search). The evaluation tests have been performed on a large image database (up to 1 million images).

机译：在本文中，我们调查了对应分析（CA）在基于内容的大规模图像检索中的大量使用。对应分析是一种用于分析文本数据的有用方法，我们使用SIFT本地描述符将其适应于图像。 CA用于减小尺寸并限制在搜索步骤中要考虑的图像数量。提出了一种用于CA的增量算法来处理大型数据库，其结果与标准算法完全相同。我们还将上下文差异度量集成到我们的搜索方案中，以提高响应时间和准确性。我们以两种方式探索这种集成：（i）离线（离线校正图像邻域的结构）和（ii）动态（在搜索过程中调整图像邻域的结构）。评估测试已在大型图像数据库（最多100万张图像）上进行。

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来源
《Advances in knowledge discovery and management》|2009年|P.57-76|共20页
会议地点 Strasbourg(FR)
作者
Nguyen-Khang Pham; rnAnnie Morin; rnPatrick Gros; rnQuyet-Thang Le;
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作者单位

University of Rennes I, IRISA, Campus de Beaulieu, 35042 RENNES Cedex, France and Cantho University, 1 Ly Tu Trong, Cantho, Vietnam;

rnUniversity of Rennes I, IRISA, Campus de Beaulieu, 35042 RENNES Cedex, France;

rnINRIA Rennes - Bretagne Atlantique, Campus de Beaulieu, 35042 RENNES Cedex, France;

rnCantho University, 1 Ly Tu Trong, Cantho, Vietnam;

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原文格式 PDF
正文语种 eng
中图分类数据处理、数据处理系统;
关键词
correspondence analysis; large scale image retrieval; image indexing; incremental algorithm; SIFT;

机译：对应分析；大规模图像检索；图像索引；增量算法；筛;

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1. Analysis of human brain by magnetic resonance imaging using content-based image retrieval [J] . Qaim Mehdi Rizvi International Journal of Health Sciences . 2020,第2期

机译：基于内容的图像检索磁共振成像分析人脑
2. Content-based image retrieval for brain MRI: An image-searching engine and population-based analysis to utilize past clinical data for future diagnosis [J] . Andreia V. Faria, Kenichi Oishi, Shoko Yoshida, NeuroImage: Clinical . 2015,第6期

机译：基于内容的脑MRI图像检索：图像搜索引擎和基于人群的分析，可利用过去的临床数据进行未来诊断
3. ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology. [J] . Foran DJ, Yang L, Chen W, Journal of the American Medical Informatics Association : . 2011,第4期

机译：ImageMiner：使用基于内容的图像检索，高性能计算和网格技术对组织微阵列进行比较分析的软件系统。
4. Intensive Use of Correspondence Analysis for Large Scale Content-Based Image Retrieval [C] . Nguyen-Khang Pham, Annie Morin, Patrick Gros, Conference on advances in knowledge discovery and management . 2010

机译：基于大规模内容的图像检索的密集使用对应分析
5. Effective graph-based content-based image retrieval systems for large-scale and small-scale image databases [D] . Chang, Ran 2014

机译：适用于大型和小型图像数据库的基于图形的基于内容的有效图像检索系统
6. Analysis of human brain by magnetic resonance imaging using content-based image retrieval [O] . Qaim Mehdi Rizvi 2020

机译：基于内容的图像检索通过磁共振成像对人脑进行分析
7. Correspondence Analysis and Hierarchical Indexing For Content-Based Image Retrieval [O] . Milanese, Ruggero, Squire, David, Pun, Thierry 1996

机译：基于内容的图像检索的对应分析和分级索引
8. System for Indexing Multi-Spectral Satellite Images for Efficient Content-Based Retrieval [R] . Barros, J., French, J., Martin, W., 2003

机译：用于高效内容检索的多光谱卫星图像索引系统

Intensive Use of Correspondence Analysis for Large Scale Content-Based Image Retrieval

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