Fine-grained visual classification via multilayer bilinear pooling with object localization

Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the sam...
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Autor*in:	Li, Ming [verfasserIn] Lei, Lin Sun, Hao Li, Xiao Kuang, Gangyao

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2021

Schlagwörter:	Fine-grained visual classification Multilayer bilinear pooling (MLBP) Object localization Convolutional neural networks (CNNs)

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021

Übergeordnetes Werk:	Enthalten in: The visual computer - Berlin : Springer, 1985, 38(2021), 3 vom: 09. Jan., Seite 811-820
Übergeordnetes Werk:	volume:38 ; year:2021 ; number:3 ; day:09 ; month:01 ; pages:811-820

Links:	Volltext

DOI / URN:	10.1007/s00371-020-02052-8

Katalog-ID:	SPR046437398

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520			\|a Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. Experiment results on three public datasets show that our proposed method can achieve competitive performance compared with several state-of-the-art methods.
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allfields	10.1007/s00371-020-02052-8 doi (DE-627)SPR046437398 (SPR)s00371-020-02052-8-e DE-627 ger DE-627 rakwb eng Li, Ming verfasserin aut Fine-grained visual classification via multilayer bilinear pooling with object localization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. Experiment results on three public datasets show that our proposed method can achieve competitive performance compared with several state-of-the-art methods. Fine-grained visual classification (dpeaa)DE-He213 Multilayer bilinear pooling (MLBP) (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Convolutional neural networks (CNNs) (dpeaa)DE-He213 Lei, Lin aut Sun, Hao aut Li, Xiao aut Kuang, Gangyao aut Enthalten in The visual computer Berlin : Springer, 1985 38(2021), 3 vom: 09. Jan., Seite 811-820 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:38 year:2021 number:3 day:09 month:01 pages:811-820 https://dx.doi.org/10.1007/s00371-020-02052-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 3 09 01 811-820
spelling	10.1007/s00371-020-02052-8 doi (DE-627)SPR046437398 (SPR)s00371-020-02052-8-e DE-627 ger DE-627 rakwb eng Li, Ming verfasserin aut Fine-grained visual classification via multilayer bilinear pooling with object localization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. Experiment results on three public datasets show that our proposed method can achieve competitive performance compared with several state-of-the-art methods. Fine-grained visual classification (dpeaa)DE-He213 Multilayer bilinear pooling (MLBP) (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Convolutional neural networks (CNNs) (dpeaa)DE-He213 Lei, Lin aut Sun, Hao aut Li, Xiao aut Kuang, Gangyao aut Enthalten in The visual computer Berlin : Springer, 1985 38(2021), 3 vom: 09. Jan., Seite 811-820 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:38 year:2021 number:3 day:09 month:01 pages:811-820 https://dx.doi.org/10.1007/s00371-020-02052-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 3 09 01 811-820
allfields_unstemmed	10.1007/s00371-020-02052-8 doi (DE-627)SPR046437398 (SPR)s00371-020-02052-8-e DE-627 ger DE-627 rakwb eng Li, Ming verfasserin aut Fine-grained visual classification via multilayer bilinear pooling with object localization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. Experiment results on three public datasets show that our proposed method can achieve competitive performance compared with several state-of-the-art methods. Fine-grained visual classification (dpeaa)DE-He213 Multilayer bilinear pooling (MLBP) (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Convolutional neural networks (CNNs) (dpeaa)DE-He213 Lei, Lin aut Sun, Hao aut Li, Xiao aut Kuang, Gangyao aut Enthalten in The visual computer Berlin : Springer, 1985 38(2021), 3 vom: 09. Jan., Seite 811-820 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:38 year:2021 number:3 day:09 month:01 pages:811-820 https://dx.doi.org/10.1007/s00371-020-02052-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 3 09 01 811-820
allfieldsGer	10.1007/s00371-020-02052-8 doi (DE-627)SPR046437398 (SPR)s00371-020-02052-8-e DE-627 ger DE-627 rakwb eng Li, Ming verfasserin aut Fine-grained visual classification via multilayer bilinear pooling with object localization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. Experiment results on three public datasets show that our proposed method can achieve competitive performance compared with several state-of-the-art methods. Fine-grained visual classification (dpeaa)DE-He213 Multilayer bilinear pooling (MLBP) (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Convolutional neural networks (CNNs) (dpeaa)DE-He213 Lei, Lin aut Sun, Hao aut Li, Xiao aut Kuang, Gangyao aut Enthalten in The visual computer Berlin : Springer, 1985 38(2021), 3 vom: 09. Jan., Seite 811-820 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:38 year:2021 number:3 day:09 month:01 pages:811-820 https://dx.doi.org/10.1007/s00371-020-02052-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 3 09 01 811-820
allfieldsSound	10.1007/s00371-020-02052-8 doi (DE-627)SPR046437398 (SPR)s00371-020-02052-8-e DE-627 ger DE-627 rakwb eng Li, Ming verfasserin aut Fine-grained visual classification via multilayer bilinear pooling with object localization 2021 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021 Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. Experiment results on three public datasets show that our proposed method can achieve competitive performance compared with several state-of-the-art methods. Fine-grained visual classification (dpeaa)DE-He213 Multilayer bilinear pooling (MLBP) (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Convolutional neural networks (CNNs) (dpeaa)DE-He213 Lei, Lin aut Sun, Hao aut Li, Xiao aut Kuang, Gangyao aut Enthalten in The visual computer Berlin : Springer, 1985 38(2021), 3 vom: 09. Jan., Seite 811-820 (DE-627)254910734 (DE-600)1463287-1 1432-2315 nnns volume:38 year:2021 number:3 day:09 month:01 pages:811-820 https://dx.doi.org/10.1007/s00371-020-02052-8 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_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 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_2118 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_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_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 38 2021 3 09 01 811-820
language	English
source	Enthalten in The visual computer 38(2021), 3 vom: 09. Jan., Seite 811-820 volume:38 year:2021 number:3 day:09 month:01 pages:811-820
sourceStr	Enthalten in The visual computer 38(2021), 3 vom: 09. Jan., Seite 811-820 volume:38 year:2021 number:3 day:09 month:01 pages:811-820
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Fine-grained visual classification Multilayer bilinear pooling (MLBP) Object localization Convolutional neural networks (CNNs)
isfreeaccess_bool	false
container_title	The visual computer
authorswithroles_txt_mv	Li, Ming @@aut@@ Lei, Lin @@aut@@ Sun, Hao @@aut@@ Li, Xiao @@aut@@ Kuang, Gangyao @@aut@@
publishDateDaySort_date	2021-01-09T00:00:00Z
hierarchy_top_id	254910734
id	SPR046437398
language_de	englisch
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How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. 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author	Li, Ming
spellingShingle	Li, Ming misc Fine-grained visual classification misc Multilayer bilinear pooling (MLBP) misc Object localization misc Convolutional neural networks (CNNs) Fine-grained visual classification via multilayer bilinear pooling with object localization
authorStr	Li, Ming
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topic_title	Fine-grained visual classification via multilayer bilinear pooling with object localization Fine-grained visual classification (dpeaa)DE-He213 Multilayer bilinear pooling (MLBP) (dpeaa)DE-He213 Object localization (dpeaa)DE-He213 Convolutional neural networks (CNNs) (dpeaa)DE-He213
topic	misc Fine-grained visual classification misc Multilayer bilinear pooling (MLBP) misc Object localization misc Convolutional neural networks (CNNs)
topic_unstemmed	misc Fine-grained visual classification misc Multilayer bilinear pooling (MLBP) misc Object localization misc Convolutional neural networks (CNNs)
topic_browse	misc Fine-grained visual classification misc Multilayer bilinear pooling (MLBP) misc Object localization misc Convolutional neural networks (CNNs)
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title	Fine-grained visual classification via multilayer bilinear pooling with object localization
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title_full	Fine-grained visual classification via multilayer bilinear pooling with object localization
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author_browse	Li, Ming Lei, Lin Sun, Hao Li, Xiao Kuang, Gangyao
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title_sort	fine-grained visual classification via multilayer bilinear pooling with object localization
title_auth	Fine-grained visual classification via multilayer bilinear pooling with object localization
abstract	Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. Experiment results on three public datasets show that our proposed method can achieve competitive performance compared with several state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021
abstractGer	Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. Experiment results on three public datasets show that our proposed method can achieve competitive performance compared with several state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021
abstract_unstemmed	Abstract Fine-grained visual classification is a challenging task in the computer vision field. How to explore discriminative features is vital for classification. As one crucial step, exactly object localization is able to eliminate the background noises and highlight interesting objects at the same time. However, some current methods usually use bounding boxes to locate objects, that are not suitable when the poses of objects change. Furthermore, it has been demonstrated that deep features have strong feature representation capability, especially the bilinear pooling features, which achieved superior performance in fine-grained visual classification tasks. However, the bilinear features, which captured only from the last convolutional layer, have limited discriminability, especially when dealing with small-scale objects. In this paper, we propose a multilayer bilinear pooling model combined with object localization. First, a flexible and scalable object localization module is utilized to locate the interesting object in an image instead of using bounding boxes. Then the refined features are obtained by highlighting object region and suppressing background noises. While the multilayer bilinear pooling, which exploits the complementarity between different layers, is used for further extracting more discriminative features. Experiment results on three public datasets show that our proposed method can achieve competitive performance compared with several state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag GmbH, DE part of Springer Nature 2021
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container_issue	3
title_short	Fine-grained visual classification via multilayer bilinear pooling with object localization
url	https://dx.doi.org/10.1007/s00371-020-02052-8
remote_bool	true
author2	Lei, Lin Sun, Hao Li, Xiao Kuang, Gangyao
author2Str	Lei, Lin Sun, Hao Li, Xiao Kuang, Gangyao
ppnlink	254910734
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isOA_txt	false
hochschulschrift_bool	false
doi_str	10.1007/s00371-020-02052-8
up_date	2024-07-03T22:31:30.374Z
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Fine-grained visual classification via multilayer bilinear pooling with object localization

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