L1-norm global geometric consistency for partial-duplicate image retrieval

In all feature point based partial-duplicate image retrieval systems, false matching is a common issue. To tackle the problem, geometric contexts are widely applied to filter the inconsistent matches. This paper presents a novel method called ℓ₁-norm global geometric consistency. We first form the squared distance matrices of all the matched feature points, which remain invariant under translation and rotation between partial-duplicated images. Then we find the scale difference by solving a one-variable ℓ₁-norm error minimization problem, where the large sparse errors correspond to the locations of inconsistent matches. By adopting the Golden Section Search method the minimization problem can be solved efficiently. Extensive experimental results show that our method reaches higher precisions than state-of-the-art geometric verification methods in detecting inconsistent matches. Its speed is also highly competitive even when compared to local geometric consistency based methods.