Pairwise weak geometric consistency for large scale image search

State-of-the-art image search systems mostly build on bag-of-features (BOF) representation. As BOF ignores geometric relationships among local features, geometric consistency constraints have been proposed to improve search precision. However, exploiting full geometric constraints are too computational expensive. Weak geometric constraints have strong assumptions and can only deal with uniform transformations. To handle view point changes and nonrigid deformations, in this paper we present a novel pairwise weak geometric consistency constraint (P-WGC) method. It utilizes the local similarity characteristic of deformations, and measures the pairwise geometric similarity of matches between two sets of local features. Experiments performed on four famous datasets and a dataset of one million of images show a significant improvement due to P-WGC as well as its efficiency. Further improvement of search accuracy is obtained when it is combined with full geometric verification.