Abstract
A fast and memory-efficient method has been created for the dynamic mean shift (DMS) algorithm, which is an iterative mode-seeking algorithm. Running the standard DMS algorithm requires a large amount of memory because the algorithm dynamically updates all data during iterations. Therefore, it is difficult to use a conventional DMS algorithm for clustering large dataset. This difficulty is overcome by partitioning a dataset into subsets, and the resultant procedure is called a "distributed DMS algorithm", Experimental results on image segmentation show that the distributed DMS algorithm requires less memory than that of the conventionally used DMS algorithm.
Original language | English |
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Pages (from-to) | 1614-1620 |
Number of pages | 7 |
Journal | Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers |
Volume | 61 |
Issue number | 11 |
DOIs | |
Publication status | Published - Nov 2007 |
All Science Journal Classification (ASJC) codes
- Media Technology
- Computer Science Applications
- Electrical and Electronic Engineering