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EAGER: Large Scale Partial-duplicate Image Search by Post Verification of Local Feature Matching

EAGER：通过局部特征匹配后验证进行大规模部分重复图像搜索

基本信息

批准号：
1052851
负责人：
Qi Tian
金额：
$ 8.17万
依托单位：
University of Texas at San Antonio
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-01 至 2012-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1052851&HistoricalAwards=false
关键词：
EAGER Large Scale Partial duplicate

项目摘要

This project develops techniques to eliminate partial-duplicates in image search from a large scale database. The research team explores spatial representation schemes for partial-duplicate image retrieval in efficient ways. Images are represented by the Bag-of-Visual-Features model, in the similar way to text document represented by a set of text words. A novel scheme, spatial coding, is designed to encode the spatial relationships among local features in an image. Based on the spatial codes of images, verification of the initial matching of local features between images is performed. And those false matches are identified and removed effectively and efficiently. As a result, the similarity between images based on local feature matching is determined more accurately. Consequently, the retrieval performance is greatly improved. The approach enjoys the merit of scalability. It is an initial step towards billion-scale partial-duplicate image retrieval. The project is developing a real time partial-duplicate image search system with sound recall on 10 million web image database. The research of spatial coding addresses critical problems in multimedia visual information retrieval. The proposed spatial coding approaches have broad impact in various research fields and applications, such as image retrieval, image categorization and object recognition. Finally, research and education are integrated by providing research opportunity for graduate and undergraduate students to selecttheir research topics and senior projects.

该项目开发了从大型数据库中消除图像搜索中部分重复的技术。该研究小组探索了有效的方法部分重复图像检索的空间表示方案。图像由视觉特征袋模型表示，类似于由一组文本单词表示的文本文档。提出了一种新的编码方法--空间编码，用于对图像中局部特征之间的空间关系进行编码。基于图像的空间编码，对图像间局部特征的初始匹配进行了验证。这些错误的匹配被有效地识别和删除。结果，基于局部特征匹配的图像之间的相似性被更准确地确定。因此，检索性能大大提高。该方法具有可扩展性的优点。这是迈向十亿级部分重复图像检索的第一步。本项目是在1000万幅网络图像数据库上开发一个具有良好查全率的真实的部分重复图像检索系统。空间编码的研究是多媒体视觉信息检索中的关键问题。提出的空间编码方法在图像检索、图像分类和目标识别等领域有着广泛的应用。最后，通过为研究生和本科生提供研究机会来选择他们的研究课题和高级项目，将研究与教育结合起来。