Short Video Production Method and System Design Based on Machine Learning

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Xuan Liu


This study investigates the integration of machine learning techniques into short video production, aiming to revolutionize workflows and empower creators with advanced tools and functionalities. Through a comprehensive experimental evaluation, the proposed method and system design demonstrate promising results in terms of performance, usability, and potential impact on the digital media landscape. The experimental evaluation encompasses quantitative metrics and qualitative evaluations, revealing the high accuracy and effectiveness of machine learning models in tasks such as scene segmentation, object detection, and sentiment analysis. Statistical analysis demonstrates the system's ability to streamline the video production process and facilitate creative expression, with participants expressing satisfaction with its intuitive interface and automated editing capabilities. Challenges such as data availability, model robustness, and ethical considerations are identified as areas for further research and refinement. Nonetheless, this study represents a significant step towards reimagining short video production, leveraging machine learning to unlock new possibilities for visual storytelling and democratising access to creative tools. As technology evolves, ongoing collaboration and innovation will be crucial in shaping a more intelligent, accessible, and inclusive media ecosystem.

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