Biased Discriminant Analysis With Feature Line Embedding for Relevance Feedback-Based Image Retrieval
The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Ma...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Wang, Yu-Chen [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Systematik: |
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| Übergeordnetes Werk: |
Enthalten in: IEEE transactions on multimedia - New York, NY : Institute of Electrical and Electronics Engineers, 1999, 17(2015), 12, Seite 2245-2258 |
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| Übergeordnetes Werk: |
volume:17 ; year:2015 ; number:12 ; pages:2245-2258 |
| Links: |
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| DOI / URN: |
10.1109/TMM.2015.2492926 |
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| Katalog-ID: |
OLC1960757458 |
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| 520 | |a The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. | ||
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| 650 | 4 | |a low-level image features | |
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| 650 | 4 | |a Linear programming | |
| 650 | 4 | |a Yttrium | |
| 650 | 4 | |a Visualization | |
| 650 | 4 | |a content-based image retrieval (CBIR) | |
| 650 | 4 | |a Semantics | |
| 650 | 4 | |a Radio frequency | |
| 650 | 4 | |a high-level semantic concept | |
| 650 | 4 | |a Biased discriminant analysis | |
| 650 | 4 | |a relevance feedback | |
| 650 | 4 | |a feature line embedding (FLE) | |
| 700 | 1 | |a Han, Chin-Chuan |4 oth | |
| 700 | 1 | |a Hsieh, Chen-Ta |4 oth | |
| 700 | 1 | |a Chen, Ying-Nong |4 oth | |
| 700 | 1 | |a Fan, Kuo-Chin |4 oth | |
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10.1109/TMM.2015.2492926 doi PQ20160617 (DE-627)OLC1960757458 (DE-599)GBVOLC1960757458 (PRQ)c1862-d4667c1ac9a63729a11b0ca77f65fd3c610925d1549b62ca4f93b5ba8c196fbc0 (KEY)0381447520150000017001202245biaseddiscriminantanalysiswithfeaturelineembedding DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Wang, Yu-Chen verfasserin aut Biased Discriminant Analysis With Feature Line Embedding for Relevance Feedback-Based Image Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. Measurement low-level image features Learning systems Linear programming Yttrium Visualization content-based image retrieval (CBIR) Semantics Radio frequency high-level semantic concept Biased discriminant analysis relevance feedback feature line embedding (FLE) Han, Chin-Chuan oth Hsieh, Chen-Ta oth Chen, Ying-Nong oth Fan, Kuo-Chin oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 12, Seite 2245-2258 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:12 pages:2245-2258 http://dx.doi.org/10.1109/TMM.2015.2492926 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7300420 http://search.proquest.com/docview/1748964785 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 12 2245-2258 |
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10.1109/TMM.2015.2492926 doi PQ20160617 (DE-627)OLC1960757458 (DE-599)GBVOLC1960757458 (PRQ)c1862-d4667c1ac9a63729a11b0ca77f65fd3c610925d1549b62ca4f93b5ba8c196fbc0 (KEY)0381447520150000017001202245biaseddiscriminantanalysiswithfeaturelineembedding DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Wang, Yu-Chen verfasserin aut Biased Discriminant Analysis With Feature Line Embedding for Relevance Feedback-Based Image Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. Measurement low-level image features Learning systems Linear programming Yttrium Visualization content-based image retrieval (CBIR) Semantics Radio frequency high-level semantic concept Biased discriminant analysis relevance feedback feature line embedding (FLE) Han, Chin-Chuan oth Hsieh, Chen-Ta oth Chen, Ying-Nong oth Fan, Kuo-Chin oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 12, Seite 2245-2258 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:12 pages:2245-2258 http://dx.doi.org/10.1109/TMM.2015.2492926 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7300420 http://search.proquest.com/docview/1748964785 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 12 2245-2258 |
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10.1109/TMM.2015.2492926 doi PQ20160617 (DE-627)OLC1960757458 (DE-599)GBVOLC1960757458 (PRQ)c1862-d4667c1ac9a63729a11b0ca77f65fd3c610925d1549b62ca4f93b5ba8c196fbc0 (KEY)0381447520150000017001202245biaseddiscriminantanalysiswithfeaturelineembedding DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Wang, Yu-Chen verfasserin aut Biased Discriminant Analysis With Feature Line Embedding for Relevance Feedback-Based Image Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. Measurement low-level image features Learning systems Linear programming Yttrium Visualization content-based image retrieval (CBIR) Semantics Radio frequency high-level semantic concept Biased discriminant analysis relevance feedback feature line embedding (FLE) Han, Chin-Chuan oth Hsieh, Chen-Ta oth Chen, Ying-Nong oth Fan, Kuo-Chin oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 12, Seite 2245-2258 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:12 pages:2245-2258 http://dx.doi.org/10.1109/TMM.2015.2492926 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7300420 http://search.proquest.com/docview/1748964785 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 12 2245-2258 |
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10.1109/TMM.2015.2492926 doi PQ20160617 (DE-627)OLC1960757458 (DE-599)GBVOLC1960757458 (PRQ)c1862-d4667c1ac9a63729a11b0ca77f65fd3c610925d1549b62ca4f93b5ba8c196fbc0 (KEY)0381447520150000017001202245biaseddiscriminantanalysiswithfeaturelineembedding DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Wang, Yu-Chen verfasserin aut Biased Discriminant Analysis With Feature Line Embedding for Relevance Feedback-Based Image Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. Measurement low-level image features Learning systems Linear programming Yttrium Visualization content-based image retrieval (CBIR) Semantics Radio frequency high-level semantic concept Biased discriminant analysis relevance feedback feature line embedding (FLE) Han, Chin-Chuan oth Hsieh, Chen-Ta oth Chen, Ying-Nong oth Fan, Kuo-Chin oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 12, Seite 2245-2258 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:12 pages:2245-2258 http://dx.doi.org/10.1109/TMM.2015.2492926 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7300420 http://search.proquest.com/docview/1748964785 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 12 2245-2258 |
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10.1109/TMM.2015.2492926 doi PQ20160617 (DE-627)OLC1960757458 (DE-599)GBVOLC1960757458 (PRQ)c1862-d4667c1ac9a63729a11b0ca77f65fd3c610925d1549b62ca4f93b5ba8c196fbc0 (KEY)0381447520150000017001202245biaseddiscriminantanalysiswithfeaturelineembedding DE-627 ger DE-627 rakwb eng 004 DNB ST 325: AVZ rvk 54.87 bkl Wang, Yu-Chen verfasserin aut Biased Discriminant Analysis With Feature Line Embedding for Relevance Feedback-Based Image Retrieval 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. Measurement low-level image features Learning systems Linear programming Yttrium Visualization content-based image retrieval (CBIR) Semantics Radio frequency high-level semantic concept Biased discriminant analysis relevance feedback feature line embedding (FLE) Han, Chin-Chuan oth Hsieh, Chen-Ta oth Chen, Ying-Nong oth Fan, Kuo-Chin oth Enthalten in IEEE transactions on multimedia New York, NY : Institute of Electrical and Electronics Engineers, 1999 17(2015), 12, Seite 2245-2258 (DE-627)266019404 (DE-600)1467073-2 (DE-576)074960644 1520-9210 nnns volume:17 year:2015 number:12 pages:2245-2258 http://dx.doi.org/10.1109/TMM.2015.2492926 Volltext http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7300420 http://search.proquest.com/docview/1748964785 GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_4318 ST 325: 54.87 AVZ AR 17 2015 12 2245-2258 |
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ddc 004 rvk ST 325: bkl 54.87 misc Measurement misc low-level image features misc Learning systems misc Linear programming misc Yttrium misc Visualization misc content-based image retrieval (CBIR) misc Semantics misc Radio frequency misc high-level semantic concept misc Biased discriminant analysis misc relevance feedback misc feature line embedding (FLE) |
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ddc 004 rvk ST 325: bkl 54.87 misc Measurement misc low-level image features misc Learning systems misc Linear programming misc Yttrium misc Visualization misc content-based image retrieval (CBIR) misc Semantics misc Radio frequency misc high-level semantic concept misc Biased discriminant analysis misc relevance feedback misc feature line embedding (FLE) |
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ddc 004 rvk ST 325: bkl 54.87 misc Measurement misc low-level image features misc Learning systems misc Linear programming misc Yttrium misc Visualization misc content-based image retrieval (CBIR) misc Semantics misc Radio frequency misc high-level semantic concept misc Biased discriminant analysis misc relevance feedback misc feature line embedding (FLE) |
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biased discriminant analysis with feature line embedding for relevance feedback-based image retrieval |
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Biased Discriminant Analysis With Feature Line Embedding for Relevance Feedback-Based Image Retrieval |
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The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. |
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The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. |
| abstract_unstemmed |
The focus of content-based image retrieval (CBIR) is to narrow down the gap between low-level image features and high-level semantic concepts. In this paper, a biased discriminant analysis with feature line embedding (FLE-BDA) is proposed for performance enhancement in relevance feedback schemes. Maximizing the margin between relevant and irrelevant samples at local neighborhoods was the aim in this study. In reduced subspace, relevant images and query images can be quite close, while irrelevant samples are far away from relevant samples. The results of four benchmark datasets are given to show the performance of the proposed method. |
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Biased Discriminant Analysis With Feature Line Embedding for Relevance Feedback-Based Image Retrieval |
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Han, Chin-Chuan Hsieh, Chen-Ta Chen, Ying-Nong Fan, Kuo-Chin |
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