Image Retrieval Based on Anisotropic Scaling and Shearing Invariant Geometric Coherence

Abstract

Imposing a spatial coherence constraint on image matching is becoming a necessity for local feature based object retrieval. We tackle the affine invariance problem of the prior spatial coherence model and propose a novel approach for geometrically stable image retrieval. Compared with related studies focusing simply on translation, rotation, and isotropic scaling, our approach can deal with more significant transformations including anisotropic scaling and shearing. Our contribution consists of revisiting the first-order affine adaptation approach and extending its application to represent the geometric coherence of a second-order local feature structure. We comprehensively evaluated our approach using Flickr Logos 32, Holiday, and Oxford Buildings benchmarks. Extensive experimentation and comparisons with state-of-the-art spatial coherence models demonstrate the superiority of our approach in image retrieval tasks.