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

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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.