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JudPriNet: Video transition detection based on semantic relationship and Monte Carlo sampling

Intelligent and Converged Networks 2024年第2期5卷 134-146页

作者： Bo Ma Jinsong Wu Wei Qi Yan School of Engineering Computer and Mathematical SciencesAuckland University of TechnologyAuckland 1024New Zealand School of Artificial Intelligence Guilin University of Electronic TechnologyGuilin 540004Chinaand also with the Department of Electrical EngineeringUniversity of ChileSantiago 8010037Chile

Video understanding and content boundary detection are vital stages in video ***,previous content boundary detection methods require collecting information,including location,cast,action,and audio,and if any of these elements are missing,the results may be adversely *** address this issue and effectively detect transitions in video content,in this paper,we introduce a video classification and boundary detection method named *** focus of this paper is on objects in videos along with their labels,enabling automatic scene detection in video clips and establishing semantic connections among local objects in the *** a significant contribution,JudPriNet presents a framework that maps labels to“continuous bag of Visual words Model”to cluster labels and generates new standardized labels as video-type *** facilitates automatic classification of video ***,JudPriNet employs Monte Carlo sampling method to classify video clips,the features of video clips as elements within the *** proposed method seamlessly integrates video and textual components without compromising training and inference *** experimentation,we have demonstrated that JudPriNet,with its semantic connections,is able to effectively classify videos alongside textual *** results indicate that,compared with several other detection approaches,JudPriNet excels in high-level content detection without disrupting the integrity of the video content,outperforming existing methods.

关键词： video scene detection Monte Carlo object detection continuous bag-of-words

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