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
다중 인스턴스 학습(MIL)을 기반으로 하는 새로운 자동 이미지 주석(AIA) 방식이 제안되었습니다. 주어진 개념에 대해 포지티브 백(즉, 이미지)에 포함된 포지티브 인스턴스(즉, 이미지의 영역)를 효과적으로 마이닝하기 위해 MIL(MR-MIL이라고 함)에 매니폴드 순위(MR)가 먼저 사용됩니다. 마이닝된 긍정적인 인스턴스를 사용하면 SVM 분류기의 확률적 출력을 통해 개념의 의미 모델이 구축됩니다. 실험 결과는 지역 수준에서 높은 주석 정확도를 달성할 수 있음을 보여줍니다.
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Yufeng ZHAO, Yao ZHAO, Zhenfeng ZHU, Jeng-Shyang PAN, "MR-MIL: Manifold Ranking Based Multiple-Instance Learning for Automatic Image Annotation" in IEICE TRANSACTIONS on Fundamentals,
vol. E91-A, no. 10, pp. 3088-3089, October 2008, doi: 10.1093/ietfec/e91-a.10.3088.
Abstract: A novel automatic image annotation (AIA) scheme is proposed based on multiple-instance learning (MIL). For a given concept, manifold ranking (MR) is first employed to MIL (referred as MR-MIL) for effectively mining the positive instances (i.e. regions in images) embedded in the positive bags (i.e. images). With the mined positive instances, the semantic model of the concept is built by the probabilistic output of SVM classifier. The experimental results reveal that high annotation accuracy can be achieved at region-level.
URL: https://global.ieice.org/en_transactions/fundamentals/10.1093/ietfec/e91-a.10.3088/_p
부
@ARTICLE{e91-a_10_3088,
author={Yufeng ZHAO, Yao ZHAO, Zhenfeng ZHU, Jeng-Shyang PAN, },
journal={IEICE TRANSACTIONS on Fundamentals},
title={MR-MIL: Manifold Ranking Based Multiple-Instance Learning for Automatic Image Annotation},
year={2008},
volume={E91-A},
number={10},
pages={3088-3089},
abstract={A novel automatic image annotation (AIA) scheme is proposed based on multiple-instance learning (MIL). For a given concept, manifold ranking (MR) is first employed to MIL (referred as MR-MIL) for effectively mining the positive instances (i.e. regions in images) embedded in the positive bags (i.e. images). With the mined positive instances, the semantic model of the concept is built by the probabilistic output of SVM classifier. The experimental results reveal that high annotation accuracy can be achieved at region-level.},
keywords={},
doi={10.1093/ietfec/e91-a.10.3088},
ISSN={1745-1337},
month={October},}
부
TY - JOUR
TI - MR-MIL: Manifold Ranking Based Multiple-Instance Learning for Automatic Image Annotation
T2 - IEICE TRANSACTIONS on Fundamentals
SP - 3088
EP - 3089
AU - Yufeng ZHAO
AU - Yao ZHAO
AU - Zhenfeng ZHU
AU - Jeng-Shyang PAN
PY - 2008
DO - 10.1093/ietfec/e91-a.10.3088
JO - IEICE TRANSACTIONS on Fundamentals
SN - 1745-1337
VL - E91-A
IS - 10
JA - IEICE TRANSACTIONS on Fundamentals
Y1 - October 2008
AB - A novel automatic image annotation (AIA) scheme is proposed based on multiple-instance learning (MIL). For a given concept, manifold ranking (MR) is first employed to MIL (referred as MR-MIL) for effectively mining the positive instances (i.e. regions in images) embedded in the positive bags (i.e. images). With the mined positive instances, the semantic model of the concept is built by the probabilistic output of SVM classifier. The experimental results reveal that high annotation accuracy can be achieved at region-level.
ER -