Movie scenes detection with MIGSOM based on shots semi-supervised clustering

Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-act...
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Gespeichert in:

Autor*in:	Ayadi, Thouraya [verfasserIn] Ellouze, Mehdi [verfasserIn] Hamdani, Tarek M. [verfasserIn] Alimi, Adel M. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Unsupervised learning Multilevel interior growing self-organizing map Movie scenes detection Video browsing

Übergeordnetes Werk:	Enthalten in: Neural computing & applications - London : Springer, 1993, 22(2012), 7-8 vom: 12. Apr., Seite 1387-1396
Übergeordnetes Werk:	volume:22 ; year:2012 ; number:7-8 ; day:12 ; month:04 ; pages:1387-1396

Links:	Volltext

DOI / URN:	10.1007/s00521-012-0930-5

Katalog-ID:	SPR006639860

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520			\|a Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded.
650		4	\|a Unsupervised learning \|7 (dpeaa)DE-He213
650		4	\|a Multilevel interior growing self-organizing map \|7 (dpeaa)DE-He213
650		4	\|a Movie scenes detection \|7 (dpeaa)DE-He213
650		4	\|a Video browsing \|7 (dpeaa)DE-He213
700	1		\|a Ellouze, Mehdi \|e verfasserin \|4 aut
700	1		\|a Hamdani, Tarek M. \|e verfasserin \|4 aut
700	1		\|a Alimi, Adel M. \|e verfasserin \|4 aut
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matchkey_str	article:14333058:2012----::oiseedtcinihismaeososeiu
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allfields	10.1007/s00521-012-0930-5 doi (DE-627)SPR006639860 (SPR)s00521-012-0930-5-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Ayadi, Thouraya verfasserin aut Movie scenes detection with MIGSOM based on shots semi-supervised clustering 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded. Unsupervised learning (dpeaa)DE-He213 Multilevel interior growing self-organizing map (dpeaa)DE-He213 Movie scenes detection (dpeaa)DE-He213 Video browsing (dpeaa)DE-He213 Ellouze, Mehdi verfasserin aut Hamdani, Tarek M. verfasserin aut Alimi, Adel M. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 22(2012), 7-8 vom: 12. Apr., Seite 1387-1396 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:22 year:2012 number:7-8 day:12 month:04 pages:1387-1396 https://dx.doi.org/10.1007/s00521-012-0930-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_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 54.72 ASE AR 22 2012 7-8 12 04 1387-1396
spelling	10.1007/s00521-012-0930-5 doi (DE-627)SPR006639860 (SPR)s00521-012-0930-5-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Ayadi, Thouraya verfasserin aut Movie scenes detection with MIGSOM based on shots semi-supervised clustering 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded. Unsupervised learning (dpeaa)DE-He213 Multilevel interior growing self-organizing map (dpeaa)DE-He213 Movie scenes detection (dpeaa)DE-He213 Video browsing (dpeaa)DE-He213 Ellouze, Mehdi verfasserin aut Hamdani, Tarek M. verfasserin aut Alimi, Adel M. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 22(2012), 7-8 vom: 12. Apr., Seite 1387-1396 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:22 year:2012 number:7-8 day:12 month:04 pages:1387-1396 https://dx.doi.org/10.1007/s00521-012-0930-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_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 54.72 ASE AR 22 2012 7-8 12 04 1387-1396
allfields_unstemmed	10.1007/s00521-012-0930-5 doi (DE-627)SPR006639860 (SPR)s00521-012-0930-5-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Ayadi, Thouraya verfasserin aut Movie scenes detection with MIGSOM based on shots semi-supervised clustering 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded. Unsupervised learning (dpeaa)DE-He213 Multilevel interior growing self-organizing map (dpeaa)DE-He213 Movie scenes detection (dpeaa)DE-He213 Video browsing (dpeaa)DE-He213 Ellouze, Mehdi verfasserin aut Hamdani, Tarek M. verfasserin aut Alimi, Adel M. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 22(2012), 7-8 vom: 12. Apr., Seite 1387-1396 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:22 year:2012 number:7-8 day:12 month:04 pages:1387-1396 https://dx.doi.org/10.1007/s00521-012-0930-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_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 54.72 ASE AR 22 2012 7-8 12 04 1387-1396
allfieldsGer	10.1007/s00521-012-0930-5 doi (DE-627)SPR006639860 (SPR)s00521-012-0930-5-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Ayadi, Thouraya verfasserin aut Movie scenes detection with MIGSOM based on shots semi-supervised clustering 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded. Unsupervised learning (dpeaa)DE-He213 Multilevel interior growing self-organizing map (dpeaa)DE-He213 Movie scenes detection (dpeaa)DE-He213 Video browsing (dpeaa)DE-He213 Ellouze, Mehdi verfasserin aut Hamdani, Tarek M. verfasserin aut Alimi, Adel M. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 22(2012), 7-8 vom: 12. Apr., Seite 1387-1396 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:22 year:2012 number:7-8 day:12 month:04 pages:1387-1396 https://dx.doi.org/10.1007/s00521-012-0930-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_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 54.72 ASE AR 22 2012 7-8 12 04 1387-1396
allfieldsSound	10.1007/s00521-012-0930-5 doi (DE-627)SPR006639860 (SPR)s00521-012-0930-5-e DE-627 ger DE-627 rakwb eng 004 ASE 004 ASE 54.72 bkl Ayadi, Thouraya verfasserin aut Movie scenes detection with MIGSOM based on shots semi-supervised clustering 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded. Unsupervised learning (dpeaa)DE-He213 Multilevel interior growing self-organizing map (dpeaa)DE-He213 Movie scenes detection (dpeaa)DE-He213 Video browsing (dpeaa)DE-He213 Ellouze, Mehdi verfasserin aut Hamdani, Tarek M. verfasserin aut Alimi, Adel M. verfasserin aut Enthalten in Neural computing & applications London : Springer, 1993 22(2012), 7-8 vom: 12. Apr., Seite 1387-1396 (DE-627)271595574 (DE-600)1480526-1 1433-3058 nnns volume:22 year:2012 number:7-8 day:12 month:04 pages:1387-1396 https://dx.doi.org/10.1007/s00521-012-0930-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_267 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_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 54.72 ASE AR 22 2012 7-8 12 04 1387-1396
language	English
source	Enthalten in Neural computing & applications 22(2012), 7-8 vom: 12. Apr., Seite 1387-1396 volume:22 year:2012 number:7-8 day:12 month:04 pages:1387-1396
sourceStr	Enthalten in Neural computing & applications 22(2012), 7-8 vom: 12. Apr., Seite 1387-1396 volume:22 year:2012 number:7-8 day:12 month:04 pages:1387-1396
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Unsupervised learning Multilevel interior growing self-organizing map Movie scenes detection Video browsing
dewey-raw	004
isfreeaccess_bool	false
container_title	Neural computing & applications
authorswithroles_txt_mv	Ayadi, Thouraya @@aut@@ Ellouze, Mehdi @@aut@@ Hamdani, Tarek M. @@aut@@ Alimi, Adel M. @@aut@@
publishDateDaySort_date	2012-04-12T00:00:00Z
hierarchy_top_id	271595574
dewey-sort	14
id	SPR006639860
language_de	englisch
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author	Ayadi, Thouraya
spellingShingle	Ayadi, Thouraya ddc 004 bkl 54.72 misc Unsupervised learning misc Multilevel interior growing self-organizing map misc Movie scenes detection misc Video browsing Movie scenes detection with MIGSOM based on shots semi-supervised clustering
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topic_title	004 ASE 54.72 bkl Movie scenes detection with MIGSOM based on shots semi-supervised clustering Unsupervised learning (dpeaa)DE-He213 Multilevel interior growing self-organizing map (dpeaa)DE-He213 Movie scenes detection (dpeaa)DE-He213 Video browsing (dpeaa)DE-He213
topic	ddc 004 bkl 54.72 misc Unsupervised learning misc Multilevel interior growing self-organizing map misc Movie scenes detection misc Video browsing
topic_unstemmed	ddc 004 bkl 54.72 misc Unsupervised learning misc Multilevel interior growing self-organizing map misc Movie scenes detection misc Video browsing
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title	Movie scenes detection with MIGSOM based on shots semi-supervised clustering
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title_full	Movie scenes detection with MIGSOM based on shots semi-supervised clustering
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author_browse	Ayadi, Thouraya Ellouze, Mehdi Hamdani, Tarek M. Alimi, Adel M.
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title_sort	movie scenes detection with migsom based on shots semi-supervised clustering
title_auth	Movie scenes detection with MIGSOM based on shots semi-supervised clustering
abstract	Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded.
abstractGer	Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded.
abstract_unstemmed	Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. The obtained results show the merit of our approach in term of recall and precision rates and that our assumptions are well founded.
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title_short	Movie scenes detection with MIGSOM based on shots semi-supervised clustering
url	https://dx.doi.org/10.1007/s00521-012-0930-5
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author2	Ellouze, Mehdi Hamdani, Tarek M. Alimi, Adel M.
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doi_str	10.1007/s00521-012-0930-5
up_date	2024-07-03T23:59:59.451Z
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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">SPR006639860</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20220110191138.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201005s2012 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-012-0930-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR006639860</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00521-012-0930-5-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="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">54.72</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Ayadi, Thouraya</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Movie scenes detection with MIGSOM based on shots semi-supervised clustering</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</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="520" ind1=" " ind2=" "><subfield code="a">Abstract The segmentation into scenes helps users to browse movie archives and to select the interesting ones. In a given movie, we have two kinds of scenes: action scenes and non-action scenes. To detect action scenes, we rely on tempo features as motion and audio energy. However, to detect non-action scenes, we have to use the content information. In this paper, we present a new approach to detect non-action movie scenes. The main idea is the use of a new dynamic variant of the self-organizing maps called MIGSOM (Multilevel Interior Growing self-organizing maps) to detect agglomerations of shots in movie scenes. The originality of MIGSOM model lies in its architecture for evolving the structure of the network. The MIGSOM algorithm is generated by a growth process by adding nodes where it is necessary, whether from the boundaries or the interior of the map. In addition, the advantage of the proposed MIGSOM algorithm is their ability to find the best structure of the output space through the training process and to represent better the semantics of the data. Our system is tested on a varied database and compared to the classical SOM and others works. 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Movie scenes detection with MIGSOM based on shots semi-supervised clustering

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