A New Ontology Convolutional Neural Network for Extorting Essential Elements in Video Mining
Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos....
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| Autor*in: |
Ganesh, R. Karthik [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Journal of VLSI signal processing systems for signal, image and video technology - Springer Netherlands, 1989, 95(2023), 6 vom: 25. Mai, Seite 735-749 |
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| Übergeordnetes Werk: |
volume:95 ; year:2023 ; number:6 ; day:25 ; month:05 ; pages:735-749 |
| Links: |
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| DOI / URN: |
10.1007/s11265-023-01864-w |
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| 520 | |a Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. | ||
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10.1007/s11265-023-01864-w doi (DE-627)SPR052592286 (SPR)s11265-023-01864-w-e DE-627 ger DE-627 rakwb eng Ganesh, R. Karthik verfasserin aut A New Ontology Convolutional Neural Network for Extorting Essential Elements in Video Mining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. Video compression; Convolutional Neural Network (dpeaa)DE-He213 Ontology Model (dpeaa)DE-He213 Temporal Position (dpeaa)DE-He213 Semantic contents (dpeaa)DE-He213 Kanthavel, R. aut Dhaya, R. aut Robinson, Y. Harold aut Julie, E. Golden aut Kumar, Raghvendra aut Duong, Phet aut Thong, Pham Huy aut Son, Le Hoang aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 95(2023), 6 vom: 25. Mai, Seite 735-749 (DE-627)SPR018308090 nnns volume:95 year:2023 number:6 day:25 month:05 pages:735-749 https://dx.doi.org/10.1007/s11265-023-01864-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 95 2023 6 25 05 735-749 |
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10.1007/s11265-023-01864-w doi (DE-627)SPR052592286 (SPR)s11265-023-01864-w-e DE-627 ger DE-627 rakwb eng Ganesh, R. Karthik verfasserin aut A New Ontology Convolutional Neural Network for Extorting Essential Elements in Video Mining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. Video compression; Convolutional Neural Network (dpeaa)DE-He213 Ontology Model (dpeaa)DE-He213 Temporal Position (dpeaa)DE-He213 Semantic contents (dpeaa)DE-He213 Kanthavel, R. aut Dhaya, R. aut Robinson, Y. Harold aut Julie, E. Golden aut Kumar, Raghvendra aut Duong, Phet aut Thong, Pham Huy aut Son, Le Hoang aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 95(2023), 6 vom: 25. Mai, Seite 735-749 (DE-627)SPR018308090 nnns volume:95 year:2023 number:6 day:25 month:05 pages:735-749 https://dx.doi.org/10.1007/s11265-023-01864-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 95 2023 6 25 05 735-749 |
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10.1007/s11265-023-01864-w doi (DE-627)SPR052592286 (SPR)s11265-023-01864-w-e DE-627 ger DE-627 rakwb eng Ganesh, R. Karthik verfasserin aut A New Ontology Convolutional Neural Network for Extorting Essential Elements in Video Mining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. Video compression; Convolutional Neural Network (dpeaa)DE-He213 Ontology Model (dpeaa)DE-He213 Temporal Position (dpeaa)DE-He213 Semantic contents (dpeaa)DE-He213 Kanthavel, R. aut Dhaya, R. aut Robinson, Y. Harold aut Julie, E. Golden aut Kumar, Raghvendra aut Duong, Phet aut Thong, Pham Huy aut Son, Le Hoang aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 95(2023), 6 vom: 25. Mai, Seite 735-749 (DE-627)SPR018308090 nnns volume:95 year:2023 number:6 day:25 month:05 pages:735-749 https://dx.doi.org/10.1007/s11265-023-01864-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 95 2023 6 25 05 735-749 |
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10.1007/s11265-023-01864-w doi (DE-627)SPR052592286 (SPR)s11265-023-01864-w-e DE-627 ger DE-627 rakwb eng Ganesh, R. Karthik verfasserin aut A New Ontology Convolutional Neural Network for Extorting Essential Elements in Video Mining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. Video compression; Convolutional Neural Network (dpeaa)DE-He213 Ontology Model (dpeaa)DE-He213 Temporal Position (dpeaa)DE-He213 Semantic contents (dpeaa)DE-He213 Kanthavel, R. aut Dhaya, R. aut Robinson, Y. Harold aut Julie, E. Golden aut Kumar, Raghvendra aut Duong, Phet aut Thong, Pham Huy aut Son, Le Hoang aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 95(2023), 6 vom: 25. Mai, Seite 735-749 (DE-627)SPR018308090 nnns volume:95 year:2023 number:6 day:25 month:05 pages:735-749 https://dx.doi.org/10.1007/s11265-023-01864-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 95 2023 6 25 05 735-749 |
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10.1007/s11265-023-01864-w doi (DE-627)SPR052592286 (SPR)s11265-023-01864-w-e DE-627 ger DE-627 rakwb eng Ganesh, R. Karthik verfasserin aut A New Ontology Convolutional Neural Network for Extorting Essential Elements in Video Mining 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. Video compression; Convolutional Neural Network (dpeaa)DE-He213 Ontology Model (dpeaa)DE-He213 Temporal Position (dpeaa)DE-He213 Semantic contents (dpeaa)DE-He213 Kanthavel, R. aut Dhaya, R. aut Robinson, Y. Harold aut Julie, E. Golden aut Kumar, Raghvendra aut Duong, Phet aut Thong, Pham Huy aut Son, Le Hoang aut Enthalten in Journal of VLSI signal processing systems for signal, image and video technology Springer Netherlands, 1989 95(2023), 6 vom: 25. Mai, Seite 735-749 (DE-627)SPR018308090 nnns volume:95 year:2023 number:6 day:25 month:05 pages:735-749 https://dx.doi.org/10.1007/s11265-023-01864-w lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_40 GBV_ILN_2006 GBV_ILN_2027 AR 95 2023 6 25 05 735-749 |
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new ontology convolutional neural network for extorting essential elements in video mining |
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A New Ontology Convolutional Neural Network for Extorting Essential Elements in Video Mining |
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Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Abstract Nowadays, people use video compression for recreating video without affecting the quality with reduced size. In recent years, the number of video files has increased in social media, smartphones and video recording tools. It is not easy to search and retrieve specific content-based videos. With the use of advanced techniques, the video clips were retrieved based on an object, themes, people and other entities. Video communication tolerates a lot of problems despite using a restricted volume of cutting-edge methodology to accumulate huge-sized videos. For this motivation, the video compression technique is used. Extracting semantic data from a large number of video-based applications is necessary. These advanced techniques were mainly used in digital marketing, social marketing, and video editing. The semantic data analysis is aimed to extract the video by expressing it in text or speech. It is used to understand the content of the video and extract it in the form of footage or video clips through a query. The existing methodologies are insufficient and high amount of optimization cost. The speedy expansion in the available quantity of video information has increased an essential constraint to lengthen. Intellectual methodologies are used to construct and extort the semantic substance. In this paper, Convolutional Neural Network with VGGNet is developed for extorting essential elements in videos and for spatial modification within the frames. Rule-related information employs temporal associations to extort the characterizations. The dynamic movements are extorted by the Optical stream algorithm and it finds the temporal positions. The new algorithm is experimentally validated. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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