Superpixel‐Based Fast Image Segmentation
Tao Lei, Asoke Kumar Nandi · 2022
Inspired by the adaptive morphological watershed algorithm, this chapter fuses superpixel blocks with similar texture, color, and brightness characteristics into the fuzzy clustering algorithm to improve the final segmentation result. It presents a superpixel-based fast fuzzy c-means clustering (FCM) clustering algorithm that is significantly faster and more robust than state-of-the-art clustering algorithms for color image segmentation. Two main contributions are presented. The first one is that we showed the multiscale morphological gradient reconstruction operation to obtain a good superpixel image. The second one is that we incorporated a color histogram into the objective function to achieve fast image segmentation. The presented superpixel-based fast FCM (SFFCM) is tested on synthetic and real images. The experimental results demonstrate that the presented SFFCM is superior to state-of-the-art clustering algorithms because it provides the best segmentation results and requires the shortest running time.