Local and Nonlocal Color Line Models for Image Matting

Byoung-Kwang KIM; Meiguang JIN; Woo-Jin SONG

doi:10.1587/transfun.E97.A.1814

Local and Nonlocal Color Line Models for Image Matting

Byoung-Kwang KIM, Meiguang JIN, Woo-Jin SONG

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In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that are smooth, contain holes or have complex color. Experimental results show that the proposed method outperforms previous propagation-based matting methods. Further, it is competitive with sampling-based matting methods that require complex sampling or learning methods.

Publication: IEICE TRANSACTIONS on Fundamentals Vol.E97-A No.8 pp.1814-1819

Publication Date: 2014/08/01

Publicized

Online ISSN: 1745-1337

DOI: 10.1587/transfun.E97.A.1814

Type of Manuscript: LETTER

Category: Image

Authors

Byoung-Kwang KIM
  POSTECH
Meiguang JIN
  Technology and Research
Woo-Jin SONG
  POSTECH

Keyword

K nearest neighbors, color line model, smoothness, propagation-based matting

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Byoung-Kwang KIM, Meiguang JIN, Woo-Jin SONG, "Local and Nonlocal Color Line Models for Image Matting" in IEICE TRANSACTIONS on Fundamentals, vol. E97-A, no. 8, pp. 1814-1819, August 2014, doi: 10.1587/transfun.E97.A.1814.
Abstract: In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that are smooth, contain holes or have complex color. Experimental results show that the proposed method outperforms previous propagation-based matting methods. Further, it is competitive with sampling-based matting methods that require complex sampling or learning methods.
URL: https://globals.ieice.org/en_transactions/fundamentals/10.1587/transfun.E97.A.1814/_p

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@ARTICLE{e97-a_8_1814,
author={Byoung-Kwang KIM, Meiguang JIN, Woo-Jin SONG, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={Local and Nonlocal Color Line Models for Image Matting},
year={2014},
volume={E97-A},
number={8},
pages={1814-1819},
abstract={In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that are smooth, contain holes or have complex color. Experimental results show that the proposed method outperforms previous propagation-based matting methods. Further, it is competitive with sampling-based matting methods that require complex sampling or learning methods.},
keywords={},
doi={10.1587/transfun.E97.A.1814},
ISSN={1745-1337},
month={August},}

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TY - JOUR
TI - Local and Nonlocal Color Line Models for Image Matting
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 1814
EP - 1819
AU - Byoung-Kwang KIM
AU - Meiguang JIN
AU - Woo-Jin SONG
PY - 2014
DO - 10.1587/transfun.E97.A.1814
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E97-A
IS - 8
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - August 2014
AB - In this paper, we propose a new matting algorithm using local and nonlocal neighbors. We assume that K nearest neighbors satisfy the color line model that RGB distribution of the neighbors is roughly linear and combine this assumption with the local color line model that RGB distribution of local neighbors is roughly linear. Our assumptions are appropriate for various regions such as those that are smooth, contain holes or have complex color. Experimental results show that the proposed method outperforms previous propagation-based matting methods. Further, it is competitive with sampling-based matting methods that require complex sampling or learning methods.
ER -