The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
본 논문에서는 변형된 ABC(Artificial Bee Colony) 알고리즘을 이용한 다단계 임계화 컬러 영상 분할 방법을 제안한다. 본 연구에서는 ABC 알고리즘의 지역 검색 능력을 향상시키기 위해 Krill Herd 알고리즘을 구경꾼 벌 단계에 통합했습니다. 제안된 알고리즘의 이름은 크릴 떼에서 영감을 받은 수정된 인공 벌 군집 알고리즘(KABC 알고리즘)이다. 실험 결과를 통해 KABC 알고리즘의 견고성과 최적화 정확도 및 수렴 속도가 향상되었음을 확인했습니다. 본 연구에서는 컬러 이미지 분할을 위한 다중 레벨 임계값 문제를 해결하기 위해 KABC 알고리즘을 사용했습니다. 휘도 변화에 대처하기 위해 그레이 스케일 히스토그램을 사용하는 대신 HSV 공간 기반 전처리 방법을 사용하여 1차원 특징 벡터를 얻는 방법을 제안합니다. 그런 다음 KABC 알고리즘을 적용하여 특징 벡터의 임계값을 찾습니다. 마지막으로 분할 정확도를 높이기 위해 준최적 솔루션에 대한 추가 로컬 검색이 사용됩니다. 이 단계에서는 SSIM(구조적 유사성 지수 매트릭스)과 Kapur 엔트로피를 결합한 수정된 목적 함수를 사용합니다. 전처리 방법, KABC 알고리즘을 사용한 전역 최적화 및 로컬 최적화 단계가 전체 색상 이미지 분할 방법을 구성합니다. 실험 결과, 제안된 방법을 사용하면 분할 정확도가 향상되는 것으로 나타났습니다.
Sipeng ZHANG
Zhejiang University
Wei JIANG
Zhejiang University
Shin'ichi SATOH
National Institute of Informatics
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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
부
@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},}
부
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 -