Weakly-supervised object localization in unlabeled image collection
Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Qu, Yanyun [verfasserIn] Liu, Han [verfasserIn] Yang, Xiaoqing [verfasserIn] Fang, Suwen [verfasserIn] Wang, Hanzi [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2012 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia systems - Berlin : Springer, 1993, 19(2012), 1 vom: 24. Aug., Seite 51-63 |
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| Übergeordnetes Werk: |
volume:19 ; year:2012 ; number:1 ; day:24 ; month:08 ; pages:51-63 |
| Links: |
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| DOI / URN: |
10.1007/s00530-012-0293-x |
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| Katalog-ID: |
SPR006696023 |
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| 520 | |a Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images. | ||
| 650 | 4 | |a Multiple segmentations |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Multiple instance learning |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Object localization |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Image labeling |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Liu, Han |e verfasserin |4 aut | |
| 700 | 1 | |a Yang, Xiaoqing |e verfasserin |4 aut | |
| 700 | 1 | |a Fang, Suwen |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Hanzi |e verfasserin |4 aut | |
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10.1007/s00530-012-0293-x doi (DE-627)SPR006696023 (SPR)s00530-012-0293-x-e DE-627 ger DE-627 rakwb eng 004 ASE 54.87 bkl Qu, Yanyun verfasserin aut Weakly-supervised object localization in unlabeled image collection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images. Multiple segmentations (dpeaa)DE-He213 Multiple instance learning (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Image labeling (dpeaa)DE-He213 Liu, Han verfasserin aut Yang, Xiaoqing verfasserin aut Fang, Suwen verfasserin aut Wang, Hanzi verfasserin aut Enthalten in Multimedia systems Berlin : Springer, 1993 19(2012), 1 vom: 24. Aug., Seite 51-63 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:19 year:2012 number:1 day:24 month:08 pages:51-63 https://dx.doi.org/10.1007/s00530-012-0293-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 19 2012 1 24 08 51-63 |
| spelling |
10.1007/s00530-012-0293-x doi (DE-627)SPR006696023 (SPR)s00530-012-0293-x-e DE-627 ger DE-627 rakwb eng 004 ASE 54.87 bkl Qu, Yanyun verfasserin aut Weakly-supervised object localization in unlabeled image collection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images. Multiple segmentations (dpeaa)DE-He213 Multiple instance learning (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Image labeling (dpeaa)DE-He213 Liu, Han verfasserin aut Yang, Xiaoqing verfasserin aut Fang, Suwen verfasserin aut Wang, Hanzi verfasserin aut Enthalten in Multimedia systems Berlin : Springer, 1993 19(2012), 1 vom: 24. Aug., Seite 51-63 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:19 year:2012 number:1 day:24 month:08 pages:51-63 https://dx.doi.org/10.1007/s00530-012-0293-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 19 2012 1 24 08 51-63 |
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10.1007/s00530-012-0293-x doi (DE-627)SPR006696023 (SPR)s00530-012-0293-x-e DE-627 ger DE-627 rakwb eng 004 ASE 54.87 bkl Qu, Yanyun verfasserin aut Weakly-supervised object localization in unlabeled image collection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images. Multiple segmentations (dpeaa)DE-He213 Multiple instance learning (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Image labeling (dpeaa)DE-He213 Liu, Han verfasserin aut Yang, Xiaoqing verfasserin aut Fang, Suwen verfasserin aut Wang, Hanzi verfasserin aut Enthalten in Multimedia systems Berlin : Springer, 1993 19(2012), 1 vom: 24. Aug., Seite 51-63 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:19 year:2012 number:1 day:24 month:08 pages:51-63 https://dx.doi.org/10.1007/s00530-012-0293-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 19 2012 1 24 08 51-63 |
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10.1007/s00530-012-0293-x doi (DE-627)SPR006696023 (SPR)s00530-012-0293-x-e DE-627 ger DE-627 rakwb eng 004 ASE 54.87 bkl Qu, Yanyun verfasserin aut Weakly-supervised object localization in unlabeled image collection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images. Multiple segmentations (dpeaa)DE-He213 Multiple instance learning (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Image labeling (dpeaa)DE-He213 Liu, Han verfasserin aut Yang, Xiaoqing verfasserin aut Fang, Suwen verfasserin aut Wang, Hanzi verfasserin aut Enthalten in Multimedia systems Berlin : Springer, 1993 19(2012), 1 vom: 24. Aug., Seite 51-63 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:19 year:2012 number:1 day:24 month:08 pages:51-63 https://dx.doi.org/10.1007/s00530-012-0293-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 19 2012 1 24 08 51-63 |
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10.1007/s00530-012-0293-x doi (DE-627)SPR006696023 (SPR)s00530-012-0293-x-e DE-627 ger DE-627 rakwb eng 004 ASE 54.87 bkl Qu, Yanyun verfasserin aut Weakly-supervised object localization in unlabeled image collection 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images. Multiple segmentations (dpeaa)DE-He213 Multiple instance learning (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Image labeling (dpeaa)DE-He213 Liu, Han verfasserin aut Yang, Xiaoqing verfasserin aut Fang, Suwen verfasserin aut Wang, Hanzi verfasserin aut Enthalten in Multimedia systems Berlin : Springer, 1993 19(2012), 1 vom: 24. Aug., Seite 51-63 (DE-627)254638880 (DE-600)1463005-9 1432-1882 nnns volume:19 year:2012 number:1 day:24 month:08 pages:51-63 https://dx.doi.org/10.1007/s00530-012-0293-x lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 54.87 ASE AR 19 2012 1 24 08 51-63 |
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Multiple segmentations Multiple instance learning Object localization Image labeling |
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Multimedia systems |
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Qu, Yanyun @@aut@@ Liu, Han @@aut@@ Yang, Xiaoqing @@aut@@ Fang, Suwen @@aut@@ Wang, Hanzi @@aut@@ |
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2012-08-24T00:00:00Z |
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Qu, Yanyun |
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Qu, Yanyun ddc 004 bkl 54.87 misc Multiple segmentations misc Multiple instance learning misc Object localization misc Image labeling Weakly-supervised object localization in unlabeled image collection |
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004 ASE 54.87 bkl Weakly-supervised object localization in unlabeled image collection Multiple segmentations (dpeaa)DE-He213 Multiple instance learning (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Image labeling (dpeaa)DE-He213 |
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ddc 004 bkl 54.87 misc Multiple segmentations misc Multiple instance learning misc Object localization misc Image labeling |
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Weakly-supervised object localization in unlabeled image collection |
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weakly-supervised object localization in unlabeled image collection |
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Weakly-supervised object localization in unlabeled image collection |
| abstract |
Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images. |
| abstractGer |
Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images. |
| abstract_unstemmed |
Abstract Fully annotated image dataset is required for supervised learning. However, the image labeling process is laborious and monotonous. In this paper, we focus on automatic image labeling for a class-specified image dataset. We propose a weakly supervised approach to localize objects in a class of unlabelled images without using any manually labeled examples. Firstly, an image is segmented based on a multiple segmentation algorithm. Secondly, the segmented regions are mined based on the commonality and saliency to discovery the category pattern in the image. Thirdly, objects are localized based on the weakly supervised learning algorithm. To prove the effectiveness of the proposed approach, we experimentally evaluate the performance of our approach on 12 object classes of the Caltech101 dataset and 2 landmark classes collected from the Internet. The experimental results demonstrate that our approach is effective and accurate to automatically label images. |
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| title_short |
Weakly-supervised object localization in unlabeled image collection |
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https://dx.doi.org/10.1007/s00530-012-0293-x |
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true |
| author2 |
Liu, Han Yang, Xiaoqing Fang, Suwen Wang, Hanzi |
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Liu, Han Yang, Xiaoqing Fang, Suwen Wang, Hanzi |
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| doi_str |
10.1007/s00530-012-0293-x |
| up_date |
2024-07-04T00:14:47.782Z |
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| score |
7.4021244 |