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.
|Number of pages
|Kyokai Joho Imeji Zasshi/Journal of the Institute of Image Information and Television Engineers
|Published - Nov 2007
All Science Journal Classification (ASJC) codes
- Media Technology
- Computer Science Applications
- Electrical and Electronic Engineering