A proposed scenario to improve the Ncut algorithm in segmentation

Frontiers in Big Data

Big Data

Huynh Trung Hieu¹  Nhu Y. Tran²  Pham The Bao³ 

[1] Faculty of Information Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City, Vietnam;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;Information Science Faculty, Sai Gon University, Ho Chi Minh City, Vietnam;

关键词: GPU;    CPU;    parallel computing;    Ncut;    FCM;   

DOI  :  10.3389/fdata.2023.1134946

 received in 2022-12-31, accepted in 2023-02-13,  发布年份 2023

来源: Frontiers

【 摘 要 】

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.

【 授权许可】

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