Maximally Stable Corner Clusters: A novel distinguished region detector and descriptor

We propose a novel distinguished region detector called Maximally Stable Corner Cluster detector (MSCC). It is complementary to existing approaches like Harris-corner detectors, Difference of Gaussian detectors (DoG) or Maximally Stable Extremal Regions (MSER). The basic idea is to find distinguished regions by looking at clusters of interest points and using the concept of maximal stableness across scale. Additionally, we propose a novel descriptor ideally suited for regions detected by MSCC. It is based on the 2D joint occurrence histograms of corner orientations. We demonstrate its performance and compare it against other competitive detectors and descriptors recently evaluated by Mikolajczyk and Schmidt [11].