Video hashing based on appearance and attention features fusion via DBN
Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features a...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Sun, Jiande [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2016transfer abstract |
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| Schlagwörter: |
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| Umfang: |
11 |
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| Übergeordnetes Werk: |
Enthalten in: The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast - Liu, Yang ELSEVIER, 2018, an international journal, Amsterdam |
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| Übergeordnetes Werk: |
volume:213 ; year:2016 ; day:12 ; month:11 ; pages:84-94 ; extent:11 |
| Links: |
|---|
| DOI / URN: |
10.1016/j.neucom.2016.05.098 |
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ELV024602531 |
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| 520 | |a Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. | ||
| 520 | |a Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. | ||
| 650 | 7 | |a Deep belief network (DBN) |2 Elsevier | |
| 650 | 7 | |a Video hashing |2 Elsevier | |
| 650 | 7 | |a Feature fusion |2 Elsevier | |
| 650 | 7 | |a Angle of hash distance |2 Elsevier | |
| 650 | 7 | |a Visual attention |2 Elsevier | |
| 700 | 1 | |a Liu, Xiaocui |4 oth | |
| 700 | 1 | |a Wan, Wenbo |4 oth | |
| 700 | 1 | |a Li, Jing |4 oth | |
| 700 | 1 | |a Zhao, Dong |4 oth | |
| 700 | 1 | |a Zhang, Huaxiang |4 oth | |
| 773 | 0 | 8 | |i Enthalten in |n Elsevier |a Liu, Yang ELSEVIER |t The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |d 2018 |d an international journal |g Amsterdam |w (DE-627)ELV002603926 |
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10.1016/j.neucom.2016.05.098 doi GBVA2016014000028.pica (DE-627)ELV024602531 (ELSEVIER)S0925-2312(16)30725-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Jiande verfasserin aut Video hashing based on appearance and attention features fusion via DBN 2016transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Deep belief network (DBN) Elsevier Video hashing Elsevier Feature fusion Elsevier Angle of hash distance Elsevier Visual attention Elsevier Liu, Xiaocui oth Wan, Wenbo oth Li, Jing oth Zhao, Dong oth Zhang, Huaxiang oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:213 year:2016 day:12 month:11 pages:84-94 extent:11 https://doi.org/10.1016/j.neucom.2016.05.098 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 213 2016 12 1112 84-94 11 045F 610 |
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10.1016/j.neucom.2016.05.098 doi GBVA2016014000028.pica (DE-627)ELV024602531 (ELSEVIER)S0925-2312(16)30725-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Jiande verfasserin aut Video hashing based on appearance and attention features fusion via DBN 2016transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Deep belief network (DBN) Elsevier Video hashing Elsevier Feature fusion Elsevier Angle of hash distance Elsevier Visual attention Elsevier Liu, Xiaocui oth Wan, Wenbo oth Li, Jing oth Zhao, Dong oth Zhang, Huaxiang oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:213 year:2016 day:12 month:11 pages:84-94 extent:11 https://doi.org/10.1016/j.neucom.2016.05.098 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 213 2016 12 1112 84-94 11 045F 610 |
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10.1016/j.neucom.2016.05.098 doi GBVA2016014000028.pica (DE-627)ELV024602531 (ELSEVIER)S0925-2312(16)30725-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Jiande verfasserin aut Video hashing based on appearance and attention features fusion via DBN 2016transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Deep belief network (DBN) Elsevier Video hashing Elsevier Feature fusion Elsevier Angle of hash distance Elsevier Visual attention Elsevier Liu, Xiaocui oth Wan, Wenbo oth Li, Jing oth Zhao, Dong oth Zhang, Huaxiang oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:213 year:2016 day:12 month:11 pages:84-94 extent:11 https://doi.org/10.1016/j.neucom.2016.05.098 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 213 2016 12 1112 84-94 11 045F 610 |
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10.1016/j.neucom.2016.05.098 doi GBVA2016014000028.pica (DE-627)ELV024602531 (ELSEVIER)S0925-2312(16)30725-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Jiande verfasserin aut Video hashing based on appearance and attention features fusion via DBN 2016transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Deep belief network (DBN) Elsevier Video hashing Elsevier Feature fusion Elsevier Angle of hash distance Elsevier Visual attention Elsevier Liu, Xiaocui oth Wan, Wenbo oth Li, Jing oth Zhao, Dong oth Zhang, Huaxiang oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:213 year:2016 day:12 month:11 pages:84-94 extent:11 https://doi.org/10.1016/j.neucom.2016.05.098 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 213 2016 12 1112 84-94 11 045F 610 |
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10.1016/j.neucom.2016.05.098 doi GBVA2016014000028.pica (DE-627)ELV024602531 (ELSEVIER)S0925-2312(16)30725-1 DE-627 ger DE-627 rakwb eng 610 610 DE-600 570 VZ BIODIV DE-30 fid 35.70 bkl 42.12 bkl Sun, Jiande verfasserin aut Video hashing based on appearance and attention features fusion via DBN 2016transfer abstract 11 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. Deep belief network (DBN) Elsevier Video hashing Elsevier Feature fusion Elsevier Angle of hash distance Elsevier Visual attention Elsevier Liu, Xiaocui oth Wan, Wenbo oth Li, Jing oth Zhao, Dong oth Zhang, Huaxiang oth Enthalten in Elsevier Liu, Yang ELSEVIER The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast 2018 an international journal Amsterdam (DE-627)ELV002603926 volume:213 year:2016 day:12 month:11 pages:84-94 extent:11 https://doi.org/10.1016/j.neucom.2016.05.098 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U FID-BIODIV SSG-OLC-PHA 35.70 Biochemie: Allgemeines VZ 42.12 Biophysik VZ AR 213 2016 12 1112 84-94 11 045F 610 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:213 year:2016 day:12 month:11 pages:84-94 extent:11 |
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Enthalten in The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast Amsterdam volume:213 year:2016 day:12 month:11 pages:84-94 extent:11 |
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Deep belief network (DBN) Video hashing Feature fusion Angle of hash distance Visual attention |
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The TORC1 signaling pathway regulates respiration-induced mitophagy in yeast |
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Sun, Jiande @@aut@@ Liu, Xiaocui @@oth@@ Wan, Wenbo @@oth@@ Li, Jing @@oth@@ Zhao, Dong @@oth@@ Zhang, Huaxiang @@oth@@ |
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Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. |
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Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. |
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Video hashing has attracted increasing attention in the field of large-scale video retrieval. However, only low-level features or their combinations, referred to as appearance features, are used to generate the video hash in most of the existing video hashing algorithms and these kinds of features are referred to as appearance features. In this paper, a visual attention model is used to extract visual attention features, and the video hash is generated from a fusion of visual-appearance and visual-attention features via a deep belief network (DBN) to obtain representative video features. In addition, hash distance is taken as a vector to measure the similarity between hashes. BER is used as the amplitude of hash distance and the vector cosine similarity is used as the angle of hash distance. Experimental results demonstrate that the fusion of visual appearance and attention features brings about better performance of video hash on recall and precision rates, and the angle of hash distance is useful to improve the accuracy of hash matching. |
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