Tran Nhu Y, Hieu Huynh Trung, Bao Pham The
Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam.
Information Technology Faculty, Ho Chi Minh City University of Food Industry, Ho Chi Minh City, Vietnam.
Front Big Data. 2023 Mar 3;6:1134946. doi: 10.3389/fdata.2023.1134946. eCollection 2023.
In image segmentation, there are many methods to accomplish the result of segmenting an image into k clusters. However, the number of clusters k is always defined before running the process. It is defined by some observation or knowledge based on the application. In this paper, we propose a new scenario in order to define the value k clusters automatically using histogram information. This scenario is applied to Ncut algorithm and speeds up the running time by using CUDA language to parallel computing in GPU. The Ncut is improved in four steps: determination of number of clusters in segmentation, computing the similarity matrix W, computing the similarity matrix's eigenvalues, and grouping on the Fuzzy C-Means (FCM) clustering algorithm. Some experimental results are shown to prove that our scenario is 20 times faster than the Ncut algorithm while keeping the same accuracy.
在图像分割中，有许多方法可将图像分割成k个聚类。然而，聚类的数量k总是在运行该过程之前定义的。它是根据应用的一些观察或知识来定义的。在本文中，我们提出了一种新的方案，以便使用直方图信息自动定义k个聚类的值。此方案应用于Ncut算法，并通过使用CUDA语言在GPU中进行并行计算来加快运行时间。Ncut算法在四个步骤中得到改进：分割中聚类数量的确定、相似性矩阵W的计算、相似性矩阵特征值的计算以及基于模糊C均值（FCM）聚类算法的分组。一些实验结果表明，我们的方案在保持相同精度的同时，比Ncut算法快20倍。
