Fast and robust spatial matching for object retrieval

Spatial matching for visual words based object retrieval often involves generating affine transformation hypotheses and then choosing the best hypothesis to measure the spatial consistency. In existing methods, generating an affine transformation hypothesis either requires three correspondences or assumes images are taken in restricted range of viewpoints in using a single correspondence. In this paper, we propose a novel spatial matching method, in which the transformation hypothesis can be estimated from only a single correspondence without the assumption of the viewpoints from which the images are taken. Firstly, affine covariant neighborhoods(ACNs) of features are used to eliminate possible false matches. Secondly, we decompose the affine transformation into three sub-transforms and conquer each sub-transform by exploiting the shape information and the ACNs of a single pair of corresponding features. Experiment results demonstrate that this method improves the average retrieval precision evidently with less computation in comparison with the previous methods.