Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6873495 | Future Generation Computer Systems | 2016 | 22 Pages |
Abstract
The research on underwater image segmentation has to deal with the rapid increasing volume of images and videos. To handle this issue, parallel computing paradigms, such as the MapReduce framework has been proven as a viable solution. Therefore, we propose a MapReduce-based fast fuzzy c-means algorithm (MRFFCM) to paralyze the segmentation of the images. In our work, we use a two-layer distribution model to group the large-scale images and adopt an iterative MapReduce process to parallelize the FFCM algorithm. A combinational segmentation way is used to improve algorithm's efficiency. To evaluate the performance of our algorithm, we develop a small Hadoop cluster to test the MRFFCM algorithm. The experiment results demonstrate that our proposed method is effective and efficient on large-scale images. When compared to the traditional non-parallel methods, our algorithm can be expected to provide a more efficient segmentation on images with at least 13% improvement. Meanwhile, with the growth of cluster size, further improvement of the algorithm performance was also achieved. Consequently, such scalability can enable our proposed method to be used effectively in oceanic research, such as in underwater data processing systems.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Xiu Li, Jingdong Song, Fan Zhang, Xiaogang Ouyang, Samee U. Khan,