JudPriNet: Video Transition Detection Based on Semantic Relationship and Monte Carlo Sampling

Video understanding and content boundary detection are vital stages in video recommendation. However, 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 affected. To address this issue and effectively detect transitions in video content, in this paper, we introduce a video classification and boundary detection method named JudPriNet. The 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 images. As 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 tags. This facilitates automatic classification of video clips. Furthermore, JudPriNet employs Monte Carlo sampling method to classify video clips, the features of video clips as elements within the framework. This proposed method seamlessly integrates video and textual components without compromising training and inference speed. Through experimentation, we have demonstrated that JudPriNet, with its semantic connections, is able to effectively classify videos alongside textual content. Our 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.