Spatio-temporal saliency detection using objectness measure
Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aim...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Brahim, Khawla [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2019 |
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| Übergeordnetes Werk: |
Enthalten in: Signal, image and video processing - London [u.a.] : Springer, 2007, 13(2019), 6 vom: 20. Feb., Seite 1055-1062 |
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| Übergeordnetes Werk: |
volume:13 ; year:2019 ; number:6 ; day:20 ; month:02 ; pages:1055-1062 |
| Links: |
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| DOI / URN: |
10.1007/s11760-019-01445-0 |
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| Katalog-ID: |
SPR022278478 |
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| 520 | |a Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aims to find the similar salient objects is proposed. First, objectness measure is performed to highlight the regions that may contain the object of interest. Then, for each candidate, newly proposed motion distinctiveness cues and static features including contrast measure and spatial distance are used to compute saliency maps. Experiments over two widely benchmark datasets, using several evaluation metrics such as mean absolute error, F score and area under the ROC curve measures, show the efficiency of our saliency approach compared to recent state-of-the-art methods. More interestingly, our attended scenes locations are coherent with the ground truth video frames. On SegTrack v2 and Fukuchi datasets, our proposed method yielded an overall mean absolute error, respectively, of 0.0669 and 0.0794. These results indicate the potential of our proposed framework in detecting motion salient objects. | ||
| 650 | 4 | |a Spatio-temporal saliency detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Visual attention |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Motion distinctiveness |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Saliency map |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Kalboussi, Rahma |4 aut | |
| 700 | 1 | |a Abdellaoui, Mehrez |4 aut | |
| 700 | 1 | |a Douik, Ali |4 aut | |
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10.1007/s11760-019-01445-0 doi (DE-627)SPR022278478 (SPR)s11760-019-01445-0-e DE-627 ger DE-627 rakwb eng Brahim, Khawla verfasserin (orcid)0000-0003-3256-2334 aut Spatio-temporal saliency detection using objectness measure 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aims to find the similar salient objects is proposed. First, objectness measure is performed to highlight the regions that may contain the object of interest. Then, for each candidate, newly proposed motion distinctiveness cues and static features including contrast measure and spatial distance are used to compute saliency maps. Experiments over two widely benchmark datasets, using several evaluation metrics such as mean absolute error, F score and area under the ROC curve measures, show the efficiency of our saliency approach compared to recent state-of-the-art methods. More interestingly, our attended scenes locations are coherent with the ground truth video frames. On SegTrack v2 and Fukuchi datasets, our proposed method yielded an overall mean absolute error, respectively, of 0.0669 and 0.0794. These results indicate the potential of our proposed framework in detecting motion salient objects. Spatio-temporal saliency detection (dpeaa)DE-He213 Visual attention (dpeaa)DE-He213 Motion distinctiveness (dpeaa)DE-He213 Saliency map (dpeaa)DE-He213 Kalboussi, Rahma aut Abdellaoui, Mehrez aut Douik, Ali aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 13(2019), 6 vom: 20. Feb., Seite 1055-1062 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:13 year:2019 number:6 day:20 month:02 pages:1055-1062 https://dx.doi.org/10.1007/s11760-019-01445-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 6 20 02 1055-1062 |
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10.1007/s11760-019-01445-0 doi (DE-627)SPR022278478 (SPR)s11760-019-01445-0-e DE-627 ger DE-627 rakwb eng Brahim, Khawla verfasserin (orcid)0000-0003-3256-2334 aut Spatio-temporal saliency detection using objectness measure 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aims to find the similar salient objects is proposed. First, objectness measure is performed to highlight the regions that may contain the object of interest. Then, for each candidate, newly proposed motion distinctiveness cues and static features including contrast measure and spatial distance are used to compute saliency maps. Experiments over two widely benchmark datasets, using several evaluation metrics such as mean absolute error, F score and area under the ROC curve measures, show the efficiency of our saliency approach compared to recent state-of-the-art methods. More interestingly, our attended scenes locations are coherent with the ground truth video frames. On SegTrack v2 and Fukuchi datasets, our proposed method yielded an overall mean absolute error, respectively, of 0.0669 and 0.0794. These results indicate the potential of our proposed framework in detecting motion salient objects. Spatio-temporal saliency detection (dpeaa)DE-He213 Visual attention (dpeaa)DE-He213 Motion distinctiveness (dpeaa)DE-He213 Saliency map (dpeaa)DE-He213 Kalboussi, Rahma aut Abdellaoui, Mehrez aut Douik, Ali aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 13(2019), 6 vom: 20. Feb., Seite 1055-1062 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:13 year:2019 number:6 day:20 month:02 pages:1055-1062 https://dx.doi.org/10.1007/s11760-019-01445-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 6 20 02 1055-1062 |
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10.1007/s11760-019-01445-0 doi (DE-627)SPR022278478 (SPR)s11760-019-01445-0-e DE-627 ger DE-627 rakwb eng Brahim, Khawla verfasserin (orcid)0000-0003-3256-2334 aut Spatio-temporal saliency detection using objectness measure 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aims to find the similar salient objects is proposed. First, objectness measure is performed to highlight the regions that may contain the object of interest. Then, for each candidate, newly proposed motion distinctiveness cues and static features including contrast measure and spatial distance are used to compute saliency maps. Experiments over two widely benchmark datasets, using several evaluation metrics such as mean absolute error, F score and area under the ROC curve measures, show the efficiency of our saliency approach compared to recent state-of-the-art methods. More interestingly, our attended scenes locations are coherent with the ground truth video frames. On SegTrack v2 and Fukuchi datasets, our proposed method yielded an overall mean absolute error, respectively, of 0.0669 and 0.0794. These results indicate the potential of our proposed framework in detecting motion salient objects. Spatio-temporal saliency detection (dpeaa)DE-He213 Visual attention (dpeaa)DE-He213 Motion distinctiveness (dpeaa)DE-He213 Saliency map (dpeaa)DE-He213 Kalboussi, Rahma aut Abdellaoui, Mehrez aut Douik, Ali aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 13(2019), 6 vom: 20. Feb., Seite 1055-1062 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:13 year:2019 number:6 day:20 month:02 pages:1055-1062 https://dx.doi.org/10.1007/s11760-019-01445-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 6 20 02 1055-1062 |
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10.1007/s11760-019-01445-0 doi (DE-627)SPR022278478 (SPR)s11760-019-01445-0-e DE-627 ger DE-627 rakwb eng Brahim, Khawla verfasserin (orcid)0000-0003-3256-2334 aut Spatio-temporal saliency detection using objectness measure 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aims to find the similar salient objects is proposed. First, objectness measure is performed to highlight the regions that may contain the object of interest. Then, for each candidate, newly proposed motion distinctiveness cues and static features including contrast measure and spatial distance are used to compute saliency maps. Experiments over two widely benchmark datasets, using several evaluation metrics such as mean absolute error, F score and area under the ROC curve measures, show the efficiency of our saliency approach compared to recent state-of-the-art methods. More interestingly, our attended scenes locations are coherent with the ground truth video frames. On SegTrack v2 and Fukuchi datasets, our proposed method yielded an overall mean absolute error, respectively, of 0.0669 and 0.0794. These results indicate the potential of our proposed framework in detecting motion salient objects. Spatio-temporal saliency detection (dpeaa)DE-He213 Visual attention (dpeaa)DE-He213 Motion distinctiveness (dpeaa)DE-He213 Saliency map (dpeaa)DE-He213 Kalboussi, Rahma aut Abdellaoui, Mehrez aut Douik, Ali aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 13(2019), 6 vom: 20. Feb., Seite 1055-1062 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:13 year:2019 number:6 day:20 month:02 pages:1055-1062 https://dx.doi.org/10.1007/s11760-019-01445-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 6 20 02 1055-1062 |
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10.1007/s11760-019-01445-0 doi (DE-627)SPR022278478 (SPR)s11760-019-01445-0-e DE-627 ger DE-627 rakwb eng Brahim, Khawla verfasserin (orcid)0000-0003-3256-2334 aut Spatio-temporal saliency detection using objectness measure 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aims to find the similar salient objects is proposed. First, objectness measure is performed to highlight the regions that may contain the object of interest. Then, for each candidate, newly proposed motion distinctiveness cues and static features including contrast measure and spatial distance are used to compute saliency maps. Experiments over two widely benchmark datasets, using several evaluation metrics such as mean absolute error, F score and area under the ROC curve measures, show the efficiency of our saliency approach compared to recent state-of-the-art methods. More interestingly, our attended scenes locations are coherent with the ground truth video frames. On SegTrack v2 and Fukuchi datasets, our proposed method yielded an overall mean absolute error, respectively, of 0.0669 and 0.0794. These results indicate the potential of our proposed framework in detecting motion salient objects. Spatio-temporal saliency detection (dpeaa)DE-He213 Visual attention (dpeaa)DE-He213 Motion distinctiveness (dpeaa)DE-He213 Saliency map (dpeaa)DE-He213 Kalboussi, Rahma aut Abdellaoui, Mehrez aut Douik, Ali aut Enthalten in Signal, image and video processing London [u.a.] : Springer, 2007 13(2019), 6 vom: 20. Feb., Seite 1055-1062 (DE-627)546899102 (DE-600)2391619-9 1863-1711 nnns volume:13 year:2019 number:6 day:20 month:02 pages:1055-1062 https://dx.doi.org/10.1007/s11760-019-01445-0 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_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 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_2116 GBV_ILN_2118 GBV_ILN_2119 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_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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 13 2019 6 20 02 1055-1062 |
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Brahim, Khawla @@aut@@ Kalboussi, Rahma @@aut@@ Abdellaoui, Mehrez @@aut@@ Douik, Ali @@aut@@ |
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Spatio-temporal saliency detection using objectness measure |
| abstract |
Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aims to find the similar salient objects is proposed. First, objectness measure is performed to highlight the regions that may contain the object of interest. Then, for each candidate, newly proposed motion distinctiveness cues and static features including contrast measure and spatial distance are used to compute saliency maps. Experiments over two widely benchmark datasets, using several evaluation metrics such as mean absolute error, F score and area under the ROC curve measures, show the efficiency of our saliency approach compared to recent state-of-the-art methods. More interestingly, our attended scenes locations are coherent with the ground truth video frames. On SegTrack v2 and Fukuchi datasets, our proposed method yielded an overall mean absolute error, respectively, of 0.0669 and 0.0794. These results indicate the potential of our proposed framework in detecting motion salient objects. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstractGer |
Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aims to find the similar salient objects is proposed. First, objectness measure is performed to highlight the regions that may contain the object of interest. Then, for each candidate, newly proposed motion distinctiveness cues and static features including contrast measure and spatial distance are used to compute saliency maps. Experiments over two widely benchmark datasets, using several evaluation metrics such as mean absolute error, F score and area under the ROC curve measures, show the efficiency of our saliency approach compared to recent state-of-the-art methods. More interestingly, our attended scenes locations are coherent with the ground truth video frames. On SegTrack v2 and Fukuchi datasets, our proposed method yielded an overall mean absolute error, respectively, of 0.0669 and 0.0794. These results indicate the potential of our proposed framework in detecting motion salient objects. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
| abstract_unstemmed |
Abstract Spatio-temporal saliency detection has gradually gained much attention in various computer vision applications such as intelligent video advertising and visual tracking. In this paper, we present a new approach based on the spatial and temporal information of the input video frame which aims to find the similar salient objects is proposed. First, objectness measure is performed to highlight the regions that may contain the object of interest. Then, for each candidate, newly proposed motion distinctiveness cues and static features including contrast measure and spatial distance are used to compute saliency maps. Experiments over two widely benchmark datasets, using several evaluation metrics such as mean absolute error, F score and area under the ROC curve measures, show the efficiency of our saliency approach compared to recent state-of-the-art methods. More interestingly, our attended scenes locations are coherent with the ground truth video frames. On SegTrack v2 and Fukuchi datasets, our proposed method yielded an overall mean absolute error, respectively, of 0.0669 and 0.0794. These results indicate the potential of our proposed framework in detecting motion salient objects. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
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Spatio-temporal saliency detection using objectness measure |
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https://dx.doi.org/10.1007/s11760-019-01445-0 |
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Kalboussi, Rahma Abdellaoui, Mehrez Douik, Ali |
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Kalboussi, Rahma Abdellaoui, Mehrez Douik, Ali |
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| doi_str |
10.1007/s11760-019-01445-0 |
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| score |
7.400321 |