Local geometric consistency constraint for image retrieval
In state-of-the-art image retrieval systems, an image is represented by bag-of-features (BOF). As BOF representation discards geometric relationships among local features, exploiting geometric constraints as post-processing procedure has been shown to greatly improve retrieval precision. However, full geometric constraints are computationally expensive and weak geometric constraints have limited range of applications. To efficiently handle common transformations and deformations, we present a novel local geometric consistency constraint (LGC) method. It utilizes the local similarity characteristic of deformations, and measures the pairwise geometric similarity of matches between two sets of local features. Besides, we propose a new method to accurately calculate the transformation matrix between two matched features, with the information provided by their local neighbors. Experiments performed on famous datasets show the excellent performance of our method.