A New Ontology Convolutional Neural Network for Extorting Essential Elements in Video Mining
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...
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| Vydané v: | Journal of signal processing systems Ročník 95; číslo 6; s. 735 - 749 |
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| Hlavní autori: | , , , , , , , , |
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| Jazyk: | English |
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New York
Springer US
01.06.2023
Springer Nature B.V |
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| ISSN: | 1939-8018, 1939-8115 |
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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. |
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| AbstractList | 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. |
| Author | Ganesh, R. Karthik Thong, Pham Huy Kumar, Raghvendra Kanthavel, R. Robinson, Y. Harold Julie, E. Golden Son, Le Hoang Dhaya, R. Duong, Phet |
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| Cites_doi | 10.1109/93.556537 10.1016/j.sigpro.2017.02.013 10.1109/TIM.2014.2299371 10.1109/JBHI.2015.2437396 10.1016/S0079-6123(06)55002-2 10.1016/j.image.2015.04.014 10.1007/s11263-011-0512-5 10.1016/j.compeleceng.2013.10.005 10.1080/09540091.2021.2006146 10.1109/TIFS.2010.2080675 10.1109/TIP.2014.2336549 10.1109/TPAMI.2009.77 10.1016/j.compbiomed.2017.08.022 10.1109/EIC.2015.7230722 10.1109/ICME.2009.5202577 10.1109/MNET.2018.1700394 10.1007/s11265-020-01614-2 10.1007/s00138-018-0942-y 10.1016/j.inffus.2021.06.003 10.5121/ijsc.2011.2407 10.1109/CVPR.2015.7298961 10.5220/0005206402010209 10.1137/080738970 10.1109/TKDE.2011.189 10.1109/MobileCloud.2017.9 10.1007/s00530-021-00881-8 10.1109/IST.2012.6295575 10.1109/ICCV.2011.6126542 10.1109/ICCV.2013.273 10.1109/ICCV.1999.790410 10.1109/CVPR.2005.38 10.1007/s11263-012-0515-x |
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| Keywords | Temporal Position Video compression; Convolutional Neural Network Ontology Model Semantic contents |
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| References | Tsai, Flagg, Nakazawa, Rehg (CR27) 2012; 100 Dubey, Singh, Singh (CR9) 2016; 20 Tola, Lepetit, Fua (CR23) 2010; 32 Tiwari, Kanhangad, Pachori (CR10) 2017; 53 Acharya, Oh, Hagiwara, Tan, Adam, Gertych, Tan (CR6) 2017; 89 CR17 CR39 Oliva, Torralba (CR15) 2006; 155 CR16 CR38 CR37 CR36 CR13 CR35 CR34 CR33 CR32 CR31 Bay, Tuytelaars, Van Gool (CR14) 2006 Wold, Blum, Keislar, Wheaten (CR12) 1996; 3 Fadaei, Amirfattahi, Ahmadzadeh (CR11) 2017; 137 CR4 CR3 CR5 CR8 Li, Liu, Zhang, Meur, Shen (CR19) 2015; 38 CR7 CR29 CR28 Fallahpour, Shirmohammadi, Semsarzadeh, Zhao (CR2) 2014; 63 CR26 CR25 CR24 Ejaz, Mehmood, Baik (CR18) 2014; 40 CR22 CR21 CR40 Wei, Liu, Zhu, Zhang, Hsieh (CR30) 2022; 34 Huang, Yang, Hsu (CR1) 2010; 5 Fang, Wang, Lin, Fang (CR20) 2014; 23 HY Huang (1864_CR1) 2010; 5 1864_CR40 M Fallahpour (1864_CR2) 2014; 63 A Oliva (1864_CR15) 2006; 155 1864_CR22 1864_CR21 1864_CR24 1864_CR26 1864_CR25 1864_CR28 SR Dubey (1864_CR9) 2016; 20 1864_CR29 S Fadaei (1864_CR11) 2017; 137 E Wold (1864_CR12) 1996; 3 Y Fang (1864_CR20) 2014; 23 D Tsai (1864_CR27) 2012; 100 E Tola (1864_CR23) 2010; 32 Z Wei (1864_CR30) 2022; 34 1864_CR31 N Ejaz (1864_CR18) 2014; 40 1864_CR33 1864_CR5 1864_CR32 1864_CR8 1864_CR13 1864_CR35 1864_CR7 1864_CR34 1864_CR37 1864_CR36 1864_CR4 1864_CR17 1864_CR39 1864_CR3 UR Acharya (1864_CR6) 2017; 89 1864_CR16 J Li (1864_CR19) 2015; 38 1864_CR38 H Bay (1864_CR14) 2006 AK Tiwari (1864_CR10) 2017; 53 |
| References_xml | – ident: CR22 – volume: 3 start-page: 27 issue: 3 year: 1996 end-page: 36 ident: CR12 article-title: Content-based classification, search, and retrieval of audio publication-title: IEEE Transactions on Multimedia doi: 10.1109/93.556537 – ident: CR4 – ident: CR39 – ident: CR16 – ident: CR37 – ident: CR33 – volume: 137 start-page: 274 year: 2017 end-page: 286 ident: CR11 article-title: Local derivative radial patterns: A new texture descriptor for content-based image retrieval publication-title: Signal Processing doi: 10.1016/j.sigpro.2017.02.013 – ident: CR35 – ident: CR29 – volume: 63 start-page: 1057 issue: 5 year: 2014 end-page: 1072 ident: CR2 article-title: Tampering detection in compressed digital video using watermarking publication-title: IEEE Transactions on Instrumentation and Measurement doi: 10.1109/TIM.2014.2299371 – ident: CR8 – volume: 20 start-page: 1139 issue: 4 year: 2016 end-page: 1147 ident: CR9 article-title: Local Bit-Plane Decoded Pattern: A Novel Feature Descriptor for Biomedical Image Retrieval publication-title: IEEE Journal of Biomedical and Health Informatics doi: 10.1109/JBHI.2015.2437396 – ident: CR40 – ident: CR25 – volume: 155 start-page: 23 year: 2006 end-page: 36 ident: CR15 article-title: Building the gist of a scene: The role of global image features in recognition publication-title: Progress in Brain Research doi: 10.1016/S0079-6123(06)55002-2 – ident: CR21 – volume: 38 start-page: 100 year: 2015 end-page: 114 ident: CR19 article-title: Spatiotemporal saliency detection based on superpixel-level trajectory publication-title: Signal Processing Image Commununication doi: 10.1016/j.image.2015.04.014 – volume: 100 start-page: 190 year: 2012 end-page: 202 ident: CR27 article-title: Motion coherent tracking using multi-label MRF optimization publication-title: International Journal of Computer Vision doi: 10.1007/s11263-011-0512-5 – ident: CR3 – ident: CR38 – volume: 40 start-page: 993 year: 2014 end-page: 1005 ident: CR18 article-title: Feature aggregation based visual attention model for video summarization publication-title: Computers & Electrical Engineering doi: 10.1016/j.compeleceng.2013.10.005 – volume: 53 start-page: 73 year: 2017 end-page: 85 ident: CR10 article-title: Histogram refinement for texture descriptor based image retrieval publication-title: Signal Process: Image Communication – ident: CR17 – ident: CR31 – ident: CR13 – volume: 34 start-page: 409 issue: 1 year: 2022 end-page: 428 ident: CR30 article-title: Sentiment classification of Chinese Weibo based on extended sentiment dictionary and organisational structure of comments publication-title: Connection Science doi: 10.1080/09540091.2021.2006146 – volume: 5 start-page: 625 issue: 4 year: 2010 end-page: 637 ident: CR1 article-title: A video watermarking technique based on pseudo-3-D DCT and quantization index modulation publication-title: IEEE Transactions on Information Forensics and Security doi: 10.1109/TIFS.2010.2080675 – volume: 23 start-page: 3910 year: 2014 end-page: 3921 ident: CR20 article-title: Video saliency incorporating spatiotemporal cues and uncertainty weighting publication-title: IEEE Transactions on Image Processing doi: 10.1109/TIP.2014.2336549 – ident: CR32 – ident: CR34 – ident: CR36 – ident: CR5 – start-page: 404 year: 2006 end-page: 417 ident: CR14 publication-title: Surf: Speeded-up robust features – ident: CR7 – volume: 32 start-page: 815 issue: 5 year: 2010 end-page: 830 ident: CR23 article-title: Daisy: An efficient dense descriptor applied to wide-baseline stereo publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2009.77 – ident: CR28 – ident: CR26 – ident: CR24 – volume: 89 start-page: 389 year: 2017 ident: CR6 article-title: A deep convolutional neural network model to classify heartbeats publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2017.08.022 – volume: 38 start-page: 100 year: 2015 ident: 1864_CR19 publication-title: Signal Processing Image Commununication doi: 10.1016/j.image.2015.04.014 – volume: 89 start-page: 389 year: 2017 ident: 1864_CR6 publication-title: Computers in Biology and Medicine doi: 10.1016/j.compbiomed.2017.08.022 – ident: 1864_CR28 doi: 10.1109/EIC.2015.7230722 – volume: 5 start-page: 625 issue: 4 year: 2010 ident: 1864_CR1 publication-title: IEEE Transactions on Information Forensics and Security doi: 10.1109/TIFS.2010.2080675 – ident: 1864_CR25 doi: 10.1109/ICME.2009.5202577 – ident: 1864_CR5 – ident: 1864_CR7 doi: 10.1109/MNET.2018.1700394 – volume: 155 start-page: 23 year: 2006 ident: 1864_CR15 publication-title: Progress in Brain Research doi: 10.1016/S0079-6123(06)55002-2 – ident: 1864_CR40 doi: 10.1007/s11265-020-01614-2 – volume: 34 start-page: 409 issue: 1 year: 2022 ident: 1864_CR30 publication-title: Connection Science doi: 10.1080/09540091.2021.2006146 – volume: 100 start-page: 190 year: 2012 ident: 1864_CR27 publication-title: International Journal of Computer Vision doi: 10.1007/s11263-011-0512-5 – ident: 1864_CR31 doi: 10.1007/s00138-018-0942-y – ident: 1864_CR17 – ident: 1864_CR34 – ident: 1864_CR39 doi: 10.1016/j.inffus.2021.06.003 – volume: 40 start-page: 993 year: 2014 ident: 1864_CR18 publication-title: Computers & Electrical Engineering doi: 10.1016/j.compeleceng.2013.10.005 – volume: 32 start-page: 815 issue: 5 year: 2010 ident: 1864_CR23 publication-title: IEEE Transactions on Pattern Analysis and Machine Intelligence doi: 10.1109/TPAMI.2009.77 – ident: 1864_CR38 – ident: 1864_CR29 doi: 10.5121/ijsc.2011.2407 – start-page: 404 volume-title: Surf: Speeded-up robust features year: 2006 ident: 1864_CR14 – ident: 1864_CR21 doi: 10.1109/CVPR.2015.7298961 – ident: 1864_CR22 doi: 10.5220/0005206402010209 – volume: 3 start-page: 27 issue: 3 year: 1996 ident: 1864_CR12 publication-title: IEEE Transactions on Multimedia doi: 10.1109/93.556537 – ident: 1864_CR37 doi: 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| SubjectTerms | Algorithms Artificial neural networks Circuits and Systems Classification Clips Computer Imaging Data analysis Electrical Engineering Engineering Image Processing and Computer Vision Marketing Neural networks Pattern Recognition Pattern Recognition and Graphics Semantics Signal,Image and Speech Processing Video communication Video compression Video recorders Vision |
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| Title | A New Ontology Convolutional Neural Network for Extorting Essential Elements in Video Mining |
| URI | https://link.springer.com/article/10.1007/s11265-023-01864-w https://www.proquest.com/docview/3255107678 |
| Volume | 95 |
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