Image segmentation is a computer vision process involving dividing an image into segments based on shared characteristics such as colour, texture, or intensity. Semantic segmentation, specifically, is a type of image segmentation assigning a class label to each pixel in an image. This approach is widely used in autonomous driving, object detection and medical imaging.
To address such problems, we have proposed a green image segmentation approach that identifies each image segment without requiring backpropagation. The image is resized and divided into non-overlapping patches, with each patch labelled as pure or impure based on its class composition. Impure patches are iteratively enlarged and subdivided until all patches are labelled by class. This is a promising method, but it still requires further refinement to deal with incorrectly classified large patches and object boundaries.
