Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach

Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Liu, Huaping [verfasserIn] Sun, Fuchun

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2016

Schlagwörter:	Topical object discovery Dictionary learning Video summarization

Anmerkung:	© Springer Science+Business Media New York 2016

Übergeordnetes Werk:	Enthalten in: Cognitive Computation - New York, NY : Springer, 2009, 8(2016), 3 vom: 25. Jan., Seite 519-528
Übergeordnetes Werk:	volume:8 ; year:2016 ; number:3 ; day:25 ; month:01 ; pages:519-528

Links:	Volltext

DOI / URN:	10.1007/s12559-015-9381-5

Katalog-ID:	SPR026509156

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520			\|a Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods.
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allfields	10.1007/s12559-015-9381-5 doi (DE-627)SPR026509156 (SPR)s12559-015-9381-5-e DE-627 ger DE-627 rakwb eng Liu, Huaping verfasserin aut Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods. Topical object discovery (dpeaa)DE-He213 Dictionary learning (dpeaa)DE-He213 Video summarization (dpeaa)DE-He213 Sun, Fuchun aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 8(2016), 3 vom: 25. Jan., Seite 519-528 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:8 year:2016 number:3 day:25 month:01 pages:519-528 https://dx.doi.org/10.1007/s12559-015-9381-5 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_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_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 8 2016 3 25 01 519-528
spelling	10.1007/s12559-015-9381-5 doi (DE-627)SPR026509156 (SPR)s12559-015-9381-5-e DE-627 ger DE-627 rakwb eng Liu, Huaping verfasserin aut Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods. Topical object discovery (dpeaa)DE-He213 Dictionary learning (dpeaa)DE-He213 Video summarization (dpeaa)DE-He213 Sun, Fuchun aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 8(2016), 3 vom: 25. Jan., Seite 519-528 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:8 year:2016 number:3 day:25 month:01 pages:519-528 https://dx.doi.org/10.1007/s12559-015-9381-5 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_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_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 8 2016 3 25 01 519-528
allfields_unstemmed	10.1007/s12559-015-9381-5 doi (DE-627)SPR026509156 (SPR)s12559-015-9381-5-e DE-627 ger DE-627 rakwb eng Liu, Huaping verfasserin aut Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods. Topical object discovery (dpeaa)DE-He213 Dictionary learning (dpeaa)DE-He213 Video summarization (dpeaa)DE-He213 Sun, Fuchun aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 8(2016), 3 vom: 25. Jan., Seite 519-528 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:8 year:2016 number:3 day:25 month:01 pages:519-528 https://dx.doi.org/10.1007/s12559-015-9381-5 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_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_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 8 2016 3 25 01 519-528
allfieldsGer	10.1007/s12559-015-9381-5 doi (DE-627)SPR026509156 (SPR)s12559-015-9381-5-e DE-627 ger DE-627 rakwb eng Liu, Huaping verfasserin aut Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods. Topical object discovery (dpeaa)DE-He213 Dictionary learning (dpeaa)DE-He213 Video summarization (dpeaa)DE-He213 Sun, Fuchun aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 8(2016), 3 vom: 25. Jan., Seite 519-528 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:8 year:2016 number:3 day:25 month:01 pages:519-528 https://dx.doi.org/10.1007/s12559-015-9381-5 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_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_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 8 2016 3 25 01 519-528
allfieldsSound	10.1007/s12559-015-9381-5 doi (DE-627)SPR026509156 (SPR)s12559-015-9381-5-e DE-627 ger DE-627 rakwb eng Liu, Huaping verfasserin aut Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach 2016 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer Science+Business Media New York 2016 Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods. Topical object discovery (dpeaa)DE-He213 Dictionary learning (dpeaa)DE-He213 Video summarization (dpeaa)DE-He213 Sun, Fuchun aut Enthalten in Cognitive Computation New York, NY : Springer, 2009 8(2016), 3 vom: 25. Jan., Seite 519-528 (DE-627)594779472 (DE-600)2486574-6 1866-9964 nnns volume:8 year:2016 number:3 day:25 month:01 pages:519-528 https://dx.doi.org/10.1007/s12559-015-9381-5 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_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_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 8 2016 3 25 01 519-528
language	English
source	Enthalten in Cognitive Computation 8(2016), 3 vom: 25. Jan., Seite 519-528 volume:8 year:2016 number:3 day:25 month:01 pages:519-528
sourceStr	Enthalten in Cognitive Computation 8(2016), 3 vom: 25. Jan., Seite 519-528 volume:8 year:2016 number:3 day:25 month:01 pages:519-528
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Topical object discovery Dictionary learning Video summarization
isfreeaccess_bool	false
container_title	Cognitive Computation
authorswithroles_txt_mv	Liu, Huaping @@aut@@ Sun, Fuchun @@aut@@
publishDateDaySort_date	2016-01-25T00:00:00Z
hierarchy_top_id	594779472
id	SPR026509156
language_de	englisch
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author	Liu, Huaping
spellingShingle	Liu, Huaping misc Topical object discovery misc Dictionary learning misc Video summarization Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach
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topic_title	Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach Topical object discovery (dpeaa)DE-He213 Dictionary learning (dpeaa)DE-He213 Video summarization (dpeaa)DE-He213
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title	Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach
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title_full	Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach
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title_sort	discovery of topical objects from video: a structured dictionary learning approach
title_auth	Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach
abstract	Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods. © Springer Science+Business Media New York 2016
abstractGer	Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods. © Springer Science+Business Media New York 2016
abstract_unstemmed	Abstract Automatic discovery of topical objects in video clips is a typical cognitive-related task and is essential for understanding and summarizing the video contents. In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods. © Springer Science+Business Media New York 2016
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title_short	Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach
url	https://dx.doi.org/10.1007/s12559-015-9381-5
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up_date	2024-07-03T21:11:58.503Z
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In this paper, we propose a novel framework based on structured dictionary learning for such a task. Different from existing work which utilizes multiple segmentations to coarsely obtain the object regions, we adopt the most recently developed objectness operator to extract candidate objects. Such a method exhibits a great advantage that the interested object can be more reliably segmented. A structured dictionary learning method is proposed to discover the topical objects of the video clips. Such an optimization model exploits the temporal relation between video frames and therefore leads to better performance. Further, a globally convergent algorithm is developed to solve the structured dictionary learning problem, and extensive experiments on 10 Web video clips show that the proposed method outperforms the state-of-the-art methods.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Topical object discovery</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dictionary learning</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Video summarization</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sun, Fuchun</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Cognitive Computation</subfield><subfield code="d">New York, NY : Springer, 2009</subfield><subfield code="g">8(2016), 3 vom: 25. 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Discovery of Topical Objects from Video: A Structured Dictionary Learning Approach

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