MSCC: Maximally Stable Corner Clusters

A novel distinguished region detector, complementary to existing approaches like Harris-corner detectors, Difference of Gaussian detectors (DoG) or Maximally Stable Extremal Regions (MSER) is proposed. The basic idea is to find distinguished regions by clusters of interest points. In order to determine the number of clusters we use the concept of maximal stableness across scale. Therefore, the detected regions are called: Maximally Stable Corner Clusters (MSCC). In addition to the detector, we propose a novel joint orientation histogram (JOH) descriptor ideally suited for regions detected by the MSCC detector. The descriptor is based on the 2D joint occurrence histograms of orientations. We perform a comparative detector and descriptor analysis based on the recently proposed framework of Mikolajczyk and Schmid, we present evaluation results on additional non-planar scenes and we evaluate the benefits of combining different detectors.