Hierarchically distributed dynamic mean shift

Kohei Inoue, Kiichi Urahama

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish
Title of host publication2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings
PagesI269-I272
DOIs
Publication statusPublished - Dec 1 2006
Event14th IEEE International Conference on Image Processing, ICIP 2007 - San Antonio, TX, United States
Duration: Sep 16 2007Sep 19 2007

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume1
ISSN (Print)1522-4880

Other

Other14th IEEE International Conference on Image Processing, ICIP 2007
CountryUnited States
CitySan Antonio, TX
Period9/16/079/19/07

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All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Inoue, K., & Urahama, K. (2006). Hierarchically distributed dynamic mean shift. In 2007 IEEE International Conference on Image Processing, ICIP 2007 Proceedings (pp. I269-I272). [4378943] (Proceedings - International Conference on Image Processing, ICIP; Vol. 1). https://doi.org/10.1109/ICIP.2007.4378943