Efficient Geometric Re-ranking for Mobile Visual Search

The state-of-the-art mobile visual search approaches are based on the bag-of-visual-word (BoW). As BoW representation ignores geometric relationship among the local features, a full geometric constraint like RANSAC is usually used as a post-processing step to re-rank the matched images, which has been shown to greatly improve the precision but at high computational cost. In this paper we present a novel and efficient geometric re-ranking method. Our basic idea is that the true matching local features should be not only in a similar spatial context, but also have a consistent spatial relationship, thus we simultaneously introduce

context similarity

and

spatial similarity

to describe the geometric consistency. By incorporating these two geometric constraints, the co-occurring visual words in the same spatial context can be regarded as a “visual phrase”and significantly improve the discriminative power than single visual word. To evaluate our approach, we perform experiments on Star5k and ImageNet100k dataset. The comparison with the BoW method and Soft-assignment method highlights the effectiveness of our approach in both accuracy and speed.