This paper proposes an approach to image segmentation using Iterated Graph Cuts based on local texture features of wavelet coefficients. Using Haar Wavelet based Multiresolution Analysis, the low-frequency range (smoothed image) is used for the n-link and the high-frequency range (local texture features) is used for the t-link along with the color histogram. The proposed method can segment an object region having not only noisy edges and colors similar to the background, but also heavy texture change. Experimental results illustrate the validity of our method.
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Keita FUKUDA, Tetsuya TAKIGUCHI, Yasuo ARIKI, "Graph Cuts Segmentation by Using Local Texture Features of Multiresolution Analysis" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 7, pp. 1453-1461, July 2009, doi: 10.1587/transinf.E92.D.1453.
Abstract: This paper proposes an approach to image segmentation using Iterated Graph Cuts based on local texture features of wavelet coefficients. Using Haar Wavelet based Multiresolution Analysis, the low-frequency range (smoothed image) is used for the n-link and the high-frequency range (local texture features) is used for the t-link along with the color histogram. The proposed method can segment an object region having not only noisy edges and colors similar to the background, but also heavy texture change. Experimental results illustrate the validity of our method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1453/_p
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@ARTICLE{e92-d_7_1453,
author={Keita FUKUDA, Tetsuya TAKIGUCHI, Yasuo ARIKI, },
journal={IEICE TRANSACTIONS on Information},
title={Graph Cuts Segmentation by Using Local Texture Features of Multiresolution Analysis},
year={2009},
volume={E92-D},
number={7},
pages={1453-1461},
abstract={This paper proposes an approach to image segmentation using Iterated Graph Cuts based on local texture features of wavelet coefficients. Using Haar Wavelet based Multiresolution Analysis, the low-frequency range (smoothed image) is used for the n-link and the high-frequency range (local texture features) is used for the t-link along with the color histogram. The proposed method can segment an object region having not only noisy edges and colors similar to the background, but also heavy texture change. Experimental results illustrate the validity of our method.},
keywords={},
doi={10.1587/transinf.E92.D.1453},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Graph Cuts Segmentation by Using Local Texture Features of Multiresolution Analysis
T2 - IEICE TRANSACTIONS on Information
SP - 1453
EP - 1461
AU - Keita FUKUDA
AU - Tetsuya TAKIGUCHI
AU - Yasuo ARIKI
PY - 2009
DO - 10.1587/transinf.E92.D.1453
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2009
AB - This paper proposes an approach to image segmentation using Iterated Graph Cuts based on local texture features of wavelet coefficients. Using Haar Wavelet based Multiresolution Analysis, the low-frequency range (smoothed image) is used for the n-link and the high-frequency range (local texture features) is used for the t-link along with the color histogram. The proposed method can segment an object region having not only noisy edges and colors similar to the background, but also heavy texture change. Experimental results illustrate the validity of our method.
ER -