An Overview of Parallel Computing Models for Image Segmentation with Ncut - Trần Như Ý; Huỳnh Trung Hiếu; Phạm Thế Bảo (Journal of Technical Education Science, 2024)
Image segmentation is a prerequisite in most image processing applications. There are lots of methods for image segmentation, and there are also numerous methods to evaluate the results of such segmentations. Among them, the Ncut algorithm by J. Shi using graph theory has significantly improved the efficiency of digital image processing. Most of the results indicate that the partitions of the image are all according to human vision. However, with a large image set, the algorithm will compute at a low speed; it takes time and high memory usage for the computation. The parallel model is a model that scientists are interested in and use to improve performance in image segmentation with large-size images. The article summarizes the overview of parallel models in image segmentation and the comments and evaluations for a few parallel models on the Ncut algorithm. Experimental results reveal that the time required to compute eigenvalues in the Ncut algorithm when executed in parallel on a GPU is significantly lower compared to execution on a CPU. Furthermore, as image sizes increase, the execution time on the GPU only marginally increases in comparison to CPU execution, while still yielding similar image segmentation results.