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

该项目开发了从大规模数据库中消除图像搜索中的部分重复项的技术。该研究小组以有效的方式探索了部分复制图像检索的空间表示方案。图像由视觉特征包模型表示，其方式类似于由一组文本单词表示的文本文档。设计了一种新的编码方案--空间编码，用于编码图像局部特征之间的空间关系。基于图像的空间编码，对图像间局部特征的初始匹配进行验证。并且有效和高效地识别和删除这些错误匹配。结果，更准确地确定了基于局部特征匹配的图像之间的相似度。因此，大大提高了检索性能。该方法具有可伸缩性的优点。这是迈向十亿级部分复制图像检索的第一步。该项目正在开发一个在1000万个网络图像库上进行声音召回的实时部分重复图像搜索系统。空间编码的研究解决了多媒体视觉信息检索中的关键问题。所提出的空间编码方法在图像检索、图像分类、目标识别等各个研究领域和应用中都有着广泛的影响。最后，研究与教育相结合，为研究生和本科生提供选择研究课题和高级项目的研究机会。

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.