Handling Unbalanced Data by Weighting a Gamma Distribution for Image Segmentation

Handling imbalanced data is important in optimizing image classification and segmentation, where some classes may be underrepresented relative to others. A common approach to managing this imbalance is class weighting, which assigns higher weights to minority classes to compensate for their underrepresentation. In this paper, we propose an efficient weighting method based on a probability distribution, such as the Gamma distribution. It is a flexible model that can adjust class weights based on their density and shape. By assigning a Gamma density to each class, pixels belonging to minority classes can be given more importance by adjusting the shape parameters α and the pixel density parameter β specific to the distribution. When applying Gamma weighting, each pixel in the image is adjusted according to its class's Gamma density. This makes learning more sensitive to underrepresented classes. Weighting can be applied both before and after image segmentation or transformation, providing flexibility to better manage the influence of classes on learning.