Hierarchical evolving mean-shift

Author

Surkala, Milan ; Mozdren, Karel ; Fusek, Radovan ; Sojka, Eduard

Author_Institution

FEECS, VSB-Tech. Univ. of Ostrava, Poruba, Czech Republic

fYear

2012

fDate

Sept. 30 2012-Oct. 3 2012

Firstpage

1593

Lastpage

1596

Abstract

Segmentation and filtration are widely discussed problems in image processing. Mean shift and its variants belong to the most popular methods in this area. In this paper, we propose a new variant of relatively new evolving mean shift that is based on the idea of minimization of dataset energy given by the sum of sizes of the mean-shift vectors. Our hierarchical EMS is focused on a significant reduction of computational time due to hierarchical evolution of the size of the kernel. We also present acceleration of precise point selection and vector recalculation, which can be applied also to original EMS.

Keywords

filtering theory; image segmentation; computational time reduction; dataset energy minimization; hierarchical EMS; hierarchical evolving mean-shift; image filtration; image processing; image segmentation; kernel size hierarchical evolution; mean-shift vector size sum; point selection; vector recalculation; Acceleration; Bandwidth; Energy management; Equations; Image segmentation; Kernel; Vectors; evolving; hierarchy; image; mean-shift; segmentation;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2012 19th IEEE International Conference on

Conference_Location

Orlando, FL

ISSN

1522-4880

Print_ISBN

978-1-4673-2534-9

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2012.6467179

Filename

6467179