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
Skim anotasi imej automatik (AIA) novel dicadangkan berdasarkan pembelajaran berbilang contoh (MIL). Untuk konsep tertentu, pemeringkatan manifold (MR) mula-mula digunakan untuk MIL (dirujuk sebagai MR-MIL) untuk melombong secara berkesan kejadian positif (iaitu kawasan dalam imej) yang tertanam dalam beg positif (iaitu imej). Dengan contoh positif yang dilombong, model semantik konsep dibina oleh keluaran kebarangkalian pengelas SVM. Keputusan percubaan mendedahkan bahawa ketepatan anotasi yang tinggi boleh dicapai pada peringkat rantau.
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Salinan
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
Salinan
@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},}
Salinan
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 -