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
이 편지에서 우리는 분할 정확도를 유지할 뿐만 아니라 좋은 속도를 보장할 수 있는 컬러 이미지에 대한 계층적 분할(HS) 방법을 제안합니다. 우리의 방법에서 HS는 퍼지 단순 선형 반복 클러스터링(Fuzzy SLIC)을 채택하여 과잉 분할 결과를 얻습니다. 그런 다음 HS는 FFCM(Fast Fuzzy C-Means Clustering)을 사용하여 슈퍼픽셀을 기반으로 대략적인 분할 결과를 생성합니다. 마지막으로 HS는 분할 결과를 개선하기 위해 우선 순위 큐(KPQ)를 사용하는 비반복 K-평균 클러스터링을 사용합니다. 검증 실험에서 우리는 우리의 방법을 테스트하고 이를 다양한 유형의 노이즈 하에서 Berkeley(BSD500) 벤치마크의 최첨단 이미지 분할 방법과 비교했습니다. 실험 결과는 우리의 방법이 정확성, 속도 및 견고성 측면에서 최첨단 기술을 능가한다는 것을 보여줍니다.
Chong WU
City University of Hong Kong
Le ZHANG
Tongji University
Houwang ZHANG
China University of Geosciences
Hong YAN
City University of Hong Kong
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부
Chong WU, Le ZHANG, Houwang ZHANG, Hong YAN, "Superpixel Based Hierarchical Segmentation for Color Image" in IEICE TRANSACTIONS on Information,
vol. E103-D, no. 10, pp. 2246-2249, October 2020, doi: 10.1587/transinf.2020EDL8025.
Abstract: In this letter, we propose a hierarchical segmentation (HS) method for color images, which can not only maintain the segmentation accuracy, but also ensure a good speed. In our method, HS adopts the fuzzy simple linear iterative clustering (Fuzzy SLIC) to obtain an over-segmentation result. Then, HS uses the fast fuzzy C-means clustering (FFCM) to produce the rough segmentation result based on superpixels. Finally, HS takes the non-iterative K-means clustering using priority queue (KPQ) to refine the segmentation result. In the validation experiments, we tested our method and compared it with state-of-the-art image segmentation methods on the Berkeley (BSD500) benchmark under different types of noise. The experiment results show that our method outperforms state-of-the-art techniques in terms of accuracy, speed and robustness.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDL8025/_p
부
@ARTICLE{e103-d_10_2246,
author={Chong WU, Le ZHANG, Houwang ZHANG, Hong YAN, },
journal={IEICE TRANSACTIONS on Information},
title={Superpixel Based Hierarchical Segmentation for Color Image},
year={2020},
volume={E103-D},
number={10},
pages={2246-2249},
abstract={In this letter, we propose a hierarchical segmentation (HS) method for color images, which can not only maintain the segmentation accuracy, but also ensure a good speed. In our method, HS adopts the fuzzy simple linear iterative clustering (Fuzzy SLIC) to obtain an over-segmentation result. Then, HS uses the fast fuzzy C-means clustering (FFCM) to produce the rough segmentation result based on superpixels. Finally, HS takes the non-iterative K-means clustering using priority queue (KPQ) to refine the segmentation result. In the validation experiments, we tested our method and compared it with state-of-the-art image segmentation methods on the Berkeley (BSD500) benchmark under different types of noise. The experiment results show that our method outperforms state-of-the-art techniques in terms of accuracy, speed and robustness.},
keywords={},
doi={10.1587/transinf.2020EDL8025},
ISSN={1745-1361},
month={October},}
부
TY - JOUR
TI - Superpixel Based Hierarchical Segmentation for Color Image
T2 - IEICE TRANSACTIONS on Information
SP - 2246
EP - 2249
AU - Chong WU
AU - Le ZHANG
AU - Houwang ZHANG
AU - Hong YAN
PY - 2020
DO - 10.1587/transinf.2020EDL8025
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2020
AB - In this letter, we propose a hierarchical segmentation (HS) method for color images, which can not only maintain the segmentation accuracy, but also ensure a good speed. In our method, HS adopts the fuzzy simple linear iterative clustering (Fuzzy SLIC) to obtain an over-segmentation result. Then, HS uses the fast fuzzy C-means clustering (FFCM) to produce the rough segmentation result based on superpixels. Finally, HS takes the non-iterative K-means clustering using priority queue (KPQ) to refine the segmentation result. In the validation experiments, we tested our method and compared it with state-of-the-art image segmentation methods on the Berkeley (BSD500) benchmark under different types of noise. The experiment results show that our method outperforms state-of-the-art techniques in terms of accuracy, speed and robustness.
ER -