TY - GEN
T1 - Hierarchically distributed dynamic mean shift
AU - Inoue, Kohei
AU - Urahama, Kiichi
PY - 2006
Y1 - 2006
N2 - A fast and memory-efficient method is presented for dynamic mean shift (DMS) algorithm, which is an iterative mode-seeking algorithm. The DMS algorithm requires a large amount of memory to run because it dynamically updates all samples during the iterations. Therefore, it is difficult to use the DMS for clustering a large set of samples. The difficulty of the DMS is solved by partitioning a set of samples into subsets hierarchically, and the resultant procedure is called the hierarchically distributed DMS (HDDMS). Experimental results on image segmentation show that the HDDMS requires less memory than that of the DMS.
AB - A fast and memory-efficient method is presented for dynamic mean shift (DMS) algorithm, which is an iterative mode-seeking algorithm. The DMS algorithm requires a large amount of memory to run because it dynamically updates all samples during the iterations. Therefore, it is difficult to use the DMS for clustering a large set of samples. The difficulty of the DMS is solved by partitioning a set of samples into subsets hierarchically, and the resultant procedure is called the hierarchically distributed DMS (HDDMS). Experimental results on image segmentation show that the HDDMS requires less memory than that of the DMS.
UR - http://www.scopus.com/inward/record.url?scp=48149109864&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=48149109864&partnerID=8YFLogxK
U2 - 10.1109/ICIP.2007.4378943
DO - 10.1109/ICIP.2007.4378943
M3 - Conference contribution
AN - SCOPUS:48149109864
SN - 1424414377
SN - 9781424414376
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 269
EP - 272
BT - 2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
PB - IEEE Computer Society
T2 - 14th IEEE International Conference on Image Processing, ICIP 2007
Y2 - 16 September 2007 through 19 September 2007
ER -