Strong geometrical consistency in large scale partial-duplicate image search

Abstract

The state-of-the-art partial-duplicate image search systems reply heavily on the match of local features like SIFT. Independently matching local features across two images ignores the overall geometry structure and therefore may incur many false matches. To reduce such matches, several geometry verification methods have been proposed. This paper introduces a new geometry verification method named as Strong Geometry Consistency (SGC), which uses the orientation, scale and location information of the local feature points to accurately and quickly remove the false matches. We also propose a simple scale weighting (SW) strategy, which gives feature points with larger scales greater weights, based on the intuition that a larger-scale feature point tends to be more robust for image search as it occupies a larger area of an image. Extensive experiments performed on three popular datasets show that SGC significantly outperforms state-of-the-art geometry verification methods, and SW can further boost the performance with marginal additional computation.