Maximally Visual-Homogeneous Region Detector for Large Scale Image Retrieval

Conventional local detectors often extract numerous small repeated regions in textured areas, which easily results in false matching. In order to find representative and distinctive local invariant regions, this paper proposes a Maximally Visual-Homogeneous Region (MVHR) detector. The main contributions can be summarized as 2 parts: (1) Being different from original MSER which employs single pixel intensity as ranking unit, we propose a novel sorting method based on visual homogeneity analysis on a local patch. (2) Identifying the observation scale has a close relationship with visual homogeneity analysis, a heuristic scale selection algorithm is developed to choose a proper scale according to the changes of visual homogeneity evaluation over a range of scales. Experiments demonstrate our detector can find less but representative regions with high repeatability, while still perserving competitive precision compared to the state-of-art detectors for large scale image retrieval.