Adding Affine Invariant Geometric Constraint for Partial-Duplicate Image Retrieval

The spring up of large numbers of partial-duplicate images on the internet brings a new challenge to the image retrieval systems. Rather than taking the image as a whole, researchers bundle the local visual words by MSER detector into groups and add simple relative ordering geometric constraint to the bundles. Experiments show that bundled features become much more discriminative than single feature. However, the weak geometric constraint is only applicable when there is no significant rotation between duplicate images and it couldn´t handle the circumstances of image flip or large rotation transformation. In this paper, we improve the bundled features with an affine invariant geometric constraint. It employs area ratio invariance property of affine transformation to build the affine invariant matrix for bundled visual words. Such affine invariant geometric constraint can cope well with flip, rotation or other transformations. Experimental results on the internet partial-duplicate image database verify the promotion it brings to the original bundled features approach. Since currently there is no available public corpus for partial-duplicate image retrieval, we also publish our dataset for future studies.