Abstract
Duplication of images is a common occurrence in community based data sharing systems. An image of the same scene, residing as multiple copies in the system, introduces redundancy. This paper describes a novel technique to detect such submissions by matching the Speeded Up Robust Features (SURF) of a query image to the feature set of images in the database, which are pre-computed, dimensionality reduced, and indexed. First, a set of similar images is obtained with their feature key-point correspondences by computing homography. An occurrence of duplication is verified by statistical hypothesis testing, which considers the distribution obtained by inter-key-point Euclidean distance ratios between the corresponding key-points among the query and candidate images.
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