Deep adversarial multi-label cross-modal hashing algorithm

Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship bas...
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Autor*in:	Yang, Xiaohan [verfasserIn] Wang, Zhen Liu, Wenhao Chang, Xinyi Wu, Nannan

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Cross-modal retrieval Image-text retrieval Cross-modal similarity preserving Hashing algorithm

Anmerkung:	© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Übergeordnetes Werk:	Enthalten in: International journal of multimedia information retrieval - London : Springer, 2012, 12(2023), 2 vom: 29. Juli
Übergeordnetes Werk:	volume:12 ; year:2023 ; number:2 ; day:29 ; month:07

Links:	Volltext

DOI / URN:	10.1007/s13735-023-00288-3

Katalog-ID:	SPR052580822

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520			\|a Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods.
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allfields	10.1007/s13735-023-00288-3 doi (DE-627)SPR052580822 (SPR)s13735-023-00288-3-e DE-627 ger DE-627 rakwb eng Yang, Xiaohan verfasserin aut Deep adversarial multi-label cross-modal hashing algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods. Cross-modal retrieval (dpeaa)DE-He213 Image-text retrieval (dpeaa)DE-He213 Cross-modal similarity preserving (dpeaa)DE-He213 Hashing algorithm (dpeaa)DE-He213 Wang, Zhen aut Liu, Wenhao aut Chang, Xinyi aut Wu, Nannan aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 12(2023), 2 vom: 29. Juli (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:12 year:2023 number:2 day:29 month:07 https://dx.doi.org/10.1007/s13735-023-00288-3 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_65 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_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_2244 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 12 2023 2 29 07
spelling	10.1007/s13735-023-00288-3 doi (DE-627)SPR052580822 (SPR)s13735-023-00288-3-e DE-627 ger DE-627 rakwb eng Yang, Xiaohan verfasserin aut Deep adversarial multi-label cross-modal hashing algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods. Cross-modal retrieval (dpeaa)DE-He213 Image-text retrieval (dpeaa)DE-He213 Cross-modal similarity preserving (dpeaa)DE-He213 Hashing algorithm (dpeaa)DE-He213 Wang, Zhen aut Liu, Wenhao aut Chang, Xinyi aut Wu, Nannan aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 12(2023), 2 vom: 29. Juli (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:12 year:2023 number:2 day:29 month:07 https://dx.doi.org/10.1007/s13735-023-00288-3 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_65 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_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_2244 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 12 2023 2 29 07
allfields_unstemmed	10.1007/s13735-023-00288-3 doi (DE-627)SPR052580822 (SPR)s13735-023-00288-3-e DE-627 ger DE-627 rakwb eng Yang, Xiaohan verfasserin aut Deep adversarial multi-label cross-modal hashing algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods. Cross-modal retrieval (dpeaa)DE-He213 Image-text retrieval (dpeaa)DE-He213 Cross-modal similarity preserving (dpeaa)DE-He213 Hashing algorithm (dpeaa)DE-He213 Wang, Zhen aut Liu, Wenhao aut Chang, Xinyi aut Wu, Nannan aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 12(2023), 2 vom: 29. Juli (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:12 year:2023 number:2 day:29 month:07 https://dx.doi.org/10.1007/s13735-023-00288-3 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_65 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_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_2244 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 12 2023 2 29 07
allfieldsGer	10.1007/s13735-023-00288-3 doi (DE-627)SPR052580822 (SPR)s13735-023-00288-3-e DE-627 ger DE-627 rakwb eng Yang, Xiaohan verfasserin aut Deep adversarial multi-label cross-modal hashing algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods. Cross-modal retrieval (dpeaa)DE-He213 Image-text retrieval (dpeaa)DE-He213 Cross-modal similarity preserving (dpeaa)DE-He213 Hashing algorithm (dpeaa)DE-He213 Wang, Zhen aut Liu, Wenhao aut Chang, Xinyi aut Wu, Nannan aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 12(2023), 2 vom: 29. Juli (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:12 year:2023 number:2 day:29 month:07 https://dx.doi.org/10.1007/s13735-023-00288-3 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_65 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_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_2244 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 12 2023 2 29 07
allfieldsSound	10.1007/s13735-023-00288-3 doi (DE-627)SPR052580822 (SPR)s13735-023-00288-3-e DE-627 ger DE-627 rakwb eng Yang, Xiaohan verfasserin aut Deep adversarial multi-label cross-modal hashing algorithm 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods. Cross-modal retrieval (dpeaa)DE-He213 Image-text retrieval (dpeaa)DE-He213 Cross-modal similarity preserving (dpeaa)DE-He213 Hashing algorithm (dpeaa)DE-He213 Wang, Zhen aut Liu, Wenhao aut Chang, Xinyi aut Wu, Nannan aut Enthalten in International journal of multimedia information retrieval London : Springer, 2012 12(2023), 2 vom: 29. Juli (DE-627)684967537 (DE-600)2649485-1 2192-662X nnns volume:12 year:2023 number:2 day:29 month:07 https://dx.doi.org/10.1007/s13735-023-00288-3 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_65 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_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_2244 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 12 2023 2 29 07
language	English
source	Enthalten in International journal of multimedia information retrieval 12(2023), 2 vom: 29. Juli volume:12 year:2023 number:2 day:29 month:07
sourceStr	Enthalten in International journal of multimedia information retrieval 12(2023), 2 vom: 29. Juli volume:12 year:2023 number:2 day:29 month:07
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cross-modal retrieval Image-text retrieval Cross-modal similarity preserving Hashing algorithm
isfreeaccess_bool	false
container_title	International journal of multimedia information retrieval
authorswithroles_txt_mv	Yang, Xiaohan @@aut@@ Wang, Zhen @@aut@@ Liu, Wenhao @@aut@@ Chang, Xinyi @@aut@@ Wu, Nannan @@aut@@
publishDateDaySort_date	2023-07-29T00:00:00Z
hierarchy_top_id	684967537
id	SPR052580822
language_de	englisch
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author	Yang, Xiaohan
spellingShingle	Yang, Xiaohan misc Cross-modal retrieval misc Image-text retrieval misc Cross-modal similarity preserving misc Hashing algorithm Deep adversarial multi-label cross-modal hashing algorithm
authorStr	Yang, Xiaohan
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topic_title	Deep adversarial multi-label cross-modal hashing algorithm Cross-modal retrieval (dpeaa)DE-He213 Image-text retrieval (dpeaa)DE-He213 Cross-modal similarity preserving (dpeaa)DE-He213 Hashing algorithm (dpeaa)DE-He213
topic	misc Cross-modal retrieval misc Image-text retrieval misc Cross-modal similarity preserving misc Hashing algorithm
topic_unstemmed	misc Cross-modal retrieval misc Image-text retrieval misc Cross-modal similarity preserving misc Hashing algorithm
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hierarchy_parent_title	International journal of multimedia information retrieval
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title	Deep adversarial multi-label cross-modal hashing algorithm
ctrlnum	(DE-627)SPR052580822 (SPR)s13735-023-00288-3-e
title_full	Deep adversarial multi-label cross-modal hashing algorithm
author_sort	Yang, Xiaohan
journal	International journal of multimedia information retrieval
journalStr	International journal of multimedia information retrieval
lang_code	eng
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author_browse	Yang, Xiaohan Wang, Zhen Liu, Wenhao Chang, Xinyi Wu, Nannan
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author-letter	Yang, Xiaohan
doi_str_mv	10.1007/s13735-023-00288-3
title_sort	deep adversarial multi-label cross-modal hashing algorithm
title_auth	Deep adversarial multi-label cross-modal hashing algorithm
abstract	Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstractGer	Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
abstract_unstemmed	Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
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title_short	Deep adversarial multi-label cross-modal hashing algorithm
url	https://dx.doi.org/10.1007/s13735-023-00288-3
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author2	Wang, Zhen Liu, Wenhao Chang, Xinyi Wu, Nannan
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up_date	2024-07-04T03:18:27.905Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR052580822</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20231203064621.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">230730s2023 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s13735-023-00288-3</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR052580822</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s13735-023-00288-3-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Yang, Xiaohan</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Deep adversarial multi-label cross-modal hashing algorithm</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract In recent years, more and more researchers employ the hashing algorithm to improve the large-scale cross-modal retrieval efficiency by mapping the floating-point feature into the compact binary code. However, the cross-modal hashing algorithm usually computes the similarity relationship based on single class labels, while ignoring the multi-label information. To solve the above problem, we propose the deep adversarial multi-label cross-modal hashing algorithm (DAMCH) which takes both multi-label and deep feature into consideration during establishing the cross-modal neighbor matrix. Firstly, we propose the inter- and intra-modal neighbor relationship preserving function to make the Hamming neighbor relationship be consistent with the original neighbor relationship. Secondly, we design linear classification functions to learn binary features’ semantic labels and establish the hash semantic preserving loss function to guarantee the binary features have the same semantic information as the original label. Furthermore, we establish the intra-modal adversarial loss function to minimize the information loss during mapping the floating-point feature into the compact binary code, and propose the inter-modal adversarial loss function to ensure different modal features own the same distribution. Finally, we conduct the cross-modal retrieval comparative experiments and the ablation studies on two public datasets MIRFickr and NUS-WIDE. The experimental results show that DAMCH outperforms the current state-of-the-art methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cross-modal retrieval</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Image-text retrieval</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Cross-modal similarity preserving</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Hashing algorithm</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wang, Zhen</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Liu, Wenhao</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Chang, Xinyi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Wu, Nannan</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">International journal of multimedia information retrieval</subfield><subfield code="d">London : Springer, 2012</subfield><subfield code="g">12(2023), 2 vom: 29. 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