Enhanced Hemisphere Concept for Color Pixel Classification
Van Ng, Terumasa Aoki · 2016
Most of current clustering methods are designed for general purpose other than a specific color pixel classification use. Color Line model representation emerged as the ultimate method for clustering pixels using RGB color components. However, this method is strongly sensitive to the adjustment of input parameters, which cannot conform to the frequent change of image structures and compositions. In this paper, we address this problem by introducing a hemisphere-grid based method for RGB pixel classification. Our method minimizes the reliance on user provided parameters as well as it can dynamically estimate the proper number of clusters. The properly clustering results prove the robustness and advantages of our method in classifying color pixels for unfamiliar input images.