Video saliency detection using 3D shearlet transform
Abstract Recently, visual saliency detection has received great interest. As most video saliency detection models are based on spatiotemporal mechanism, we firstly give a simple introduction of it in this paper. After discussing issues to be addressed, we present a novel framework for video saliency...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Bao, Lei [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media New York 2015 |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 75(2015), 13 vom: 23. Juni, Seite 7761-7778 |
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| Übergeordnetes Werk: |
volume:75 ; year:2015 ; number:13 ; day:23 ; month:06 ; pages:7761-7778 |
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| DOI / URN: |
10.1007/s11042-015-2692-4 |
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OLC2035023831 |
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| 520 | |a Abstract Recently, visual saliency detection has received great interest. As most video saliency detection models are based on spatiotemporal mechanism, we firstly give a simple introduction of it in this paper. After discussing issues to be addressed, we present a novel framework for video saliency detection based on 3D discrete shearlet transform. Instead of measuring saliency by fusing spatial and temporal saliency maps, the proposed model regards video as three-dimensional data. By decomposing the video with 3D discrete shearlet transform and reconstructing it on multi-scales, this multi-scale saliency detection model obtains a number of feature blocks to describe the video. Based on each feature block, every a number of successive feature maps are taken as a whole, and the global contrast is calculated to obtain the saliency maps. By fusing all the saliency maps of different levels, the saliency map is generated for each video frame. This novel framework is very simple, and experimental results on ten videos show that the proposed model outperforms lots existing models. | ||
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| 700 | 1 | |a Zheng, Yunfei |4 aut | |
| 700 | 1 | |a Li, Yang |4 aut | |
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10.1007/s11042-015-2692-4 doi (DE-627)OLC2035023831 (DE-He213)s11042-015-2692-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Bao, Lei verfasserin aut Video saliency detection using 3D shearlet transform 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Recently, visual saliency detection has received great interest. As most video saliency detection models are based on spatiotemporal mechanism, we firstly give a simple introduction of it in this paper. After discussing issues to be addressed, we present a novel framework for video saliency detection based on 3D discrete shearlet transform. Instead of measuring saliency by fusing spatial and temporal saliency maps, the proposed model regards video as three-dimensional data. By decomposing the video with 3D discrete shearlet transform and reconstructing it on multi-scales, this multi-scale saliency detection model obtains a number of feature blocks to describe the video. Based on each feature block, every a number of successive feature maps are taken as a whole, and the global contrast is calculated to obtain the saliency maps. By fusing all the saliency maps of different levels, the saliency map is generated for each video frame. This novel framework is very simple, and experimental results on ten videos show that the proposed model outperforms lots existing models. Video saliency detection 3D discrete shearlet transform Feature blocks Global probability density Zhang, Xiongwei aut Zheng, Yunfei aut Li, Yang aut Enthalten in Multimedia tools and applications Springer US, 1995 75(2015), 13 vom: 23. Juni, Seite 7761-7778 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:75 year:2015 number:13 day:23 month:06 pages:7761-7778 https://doi.org/10.1007/s11042-015-2692-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 75 2015 13 23 06 7761-7778 |
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10.1007/s11042-015-2692-4 doi (DE-627)OLC2035023831 (DE-He213)s11042-015-2692-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Bao, Lei verfasserin aut Video saliency detection using 3D shearlet transform 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media New York 2015 Abstract Recently, visual saliency detection has received great interest. As most video saliency detection models are based on spatiotemporal mechanism, we firstly give a simple introduction of it in this paper. After discussing issues to be addressed, we present a novel framework for video saliency detection based on 3D discrete shearlet transform. Instead of measuring saliency by fusing spatial and temporal saliency maps, the proposed model regards video as three-dimensional data. By decomposing the video with 3D discrete shearlet transform and reconstructing it on multi-scales, this multi-scale saliency detection model obtains a number of feature blocks to describe the video. Based on each feature block, every a number of successive feature maps are taken as a whole, and the global contrast is calculated to obtain the saliency maps. By fusing all the saliency maps of different levels, the saliency map is generated for each video frame. This novel framework is very simple, and experimental results on ten videos show that the proposed model outperforms lots existing models. Video saliency detection 3D discrete shearlet transform Feature blocks Global probability density Zhang, Xiongwei aut Zheng, Yunfei aut Li, Yang aut Enthalten in Multimedia tools and applications Springer US, 1995 75(2015), 13 vom: 23. Juni, Seite 7761-7778 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:75 year:2015 number:13 day:23 month:06 pages:7761-7778 https://doi.org/10.1007/s11042-015-2692-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW GBV_ILN_70 AR 75 2015 13 23 06 7761-7778 |
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Abstract Recently, visual saliency detection has received great interest. As most video saliency detection models are based on spatiotemporal mechanism, we firstly give a simple introduction of it in this paper. After discussing issues to be addressed, we present a novel framework for video saliency detection based on 3D discrete shearlet transform. Instead of measuring saliency by fusing spatial and temporal saliency maps, the proposed model regards video as three-dimensional data. By decomposing the video with 3D discrete shearlet transform and reconstructing it on multi-scales, this multi-scale saliency detection model obtains a number of feature blocks to describe the video. Based on each feature block, every a number of successive feature maps are taken as a whole, and the global contrast is calculated to obtain the saliency maps. By fusing all the saliency maps of different levels, the saliency map is generated for each video frame. This novel framework is very simple, and experimental results on ten videos show that the proposed model outperforms lots existing models. © Springer Science+Business Media New York 2015 |
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Abstract Recently, visual saliency detection has received great interest. As most video saliency detection models are based on spatiotemporal mechanism, we firstly give a simple introduction of it in this paper. After discussing issues to be addressed, we present a novel framework for video saliency detection based on 3D discrete shearlet transform. Instead of measuring saliency by fusing spatial and temporal saliency maps, the proposed model regards video as three-dimensional data. By decomposing the video with 3D discrete shearlet transform and reconstructing it on multi-scales, this multi-scale saliency detection model obtains a number of feature blocks to describe the video. Based on each feature block, every a number of successive feature maps are taken as a whole, and the global contrast is calculated to obtain the saliency maps. By fusing all the saliency maps of different levels, the saliency map is generated for each video frame. This novel framework is very simple, and experimental results on ten videos show that the proposed model outperforms lots existing models. © Springer Science+Business Media New York 2015 |
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Abstract Recently, visual saliency detection has received great interest. As most video saliency detection models are based on spatiotemporal mechanism, we firstly give a simple introduction of it in this paper. After discussing issues to be addressed, we present a novel framework for video saliency detection based on 3D discrete shearlet transform. Instead of measuring saliency by fusing spatial and temporal saliency maps, the proposed model regards video as three-dimensional data. By decomposing the video with 3D discrete shearlet transform and reconstructing it on multi-scales, this multi-scale saliency detection model obtains a number of feature blocks to describe the video. Based on each feature block, every a number of successive feature maps are taken as a whole, and the global contrast is calculated to obtain the saliency maps. By fusing all the saliency maps of different levels, the saliency map is generated for each video frame. This novel framework is very simple, and experimental results on ten videos show that the proposed model outperforms lots existing models. © Springer Science+Business Media New York 2015 |
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Video saliency detection using 3D shearlet transform |
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https://doi.org/10.1007/s11042-015-2692-4 |
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Zhang, Xiongwei Zheng, Yunfei Li, Yang |
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Zhang, Xiongwei Zheng, Yunfei Li, Yang |
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