IEICE TRANSACTIONS on Information
Multilevel Thresholding Color Image Segmentation Using a Modified Artificial Bee Colony Algorithm
Summary :
In this paper, a multilevel thresholding color image segmentation method is proposed using a modified Artificial Bee Colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified Artificial Bee Colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.
- Publication
- IEICE TRANSACTIONS on Information Vol.E101-D No.8 pp.2064-2071
- Publication Date
- 2018/08/01
- Publicized
- 2018/05/09
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2017EDP7183
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
Authors
Sipeng ZHANG
Zhejiang University
Wei JIANG
Zhejiang University
Shin'ichi SATOH
National Institute of Informatics
Keyword
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Sipeng ZHANG, Wei JIANG, Shin'ichi SATOH, "Multilevel Thresholding Color Image Segmentation Using a Modified Artificial Bee Colony Algorithm" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 8, pp. 2064-2071, August 2018, doi: 10.1587/transinf.2017EDP7183.
Abstract: In this paper, a multilevel thresholding color image segmentation method is proposed using a modified Artificial Bee Colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified Artificial Bee Colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2017EDP7183/_p
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@ARTICLE{e101-d_8_2064,
author={Sipeng ZHANG, Wei JIANG, Shin'ichi SATOH, },
journal={IEICE TRANSACTIONS on Information},
title={Multilevel Thresholding Color Image Segmentation Using a Modified Artificial Bee Colony Algorithm},
year={2018},
volume={E101-D},
number={8},
pages={2064-2071},
abstract={In this paper, a multilevel thresholding color image segmentation method is proposed using a modified Artificial Bee Colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified Artificial Bee Colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.},
keywords={},
doi={10.1587/transinf.2017EDP7183},
ISSN={1745-1361},
month={August},}
Copy
TY - JOUR
TI - Multilevel Thresholding Color Image Segmentation Using a Modified Artificial Bee Colony Algorithm
T2 - IEICE TRANSACTIONS on Information
SP - 2064
EP - 2071
AU - Sipeng ZHANG
AU - Wei JIANG
AU - Shin'ichi SATOH
PY - 2018
DO - 10.1587/transinf.2017EDP7183
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 8
JA - IEICE TRANSACTIONS on Information
Y1 - August 2018
AB - In this paper, a multilevel thresholding color image segmentation method is proposed using a modified Artificial Bee Colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified Artificial Bee Colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.
ER -
English
