Scholarlink

<p>Video semantic analysis is essential for interpreting audiovisual content, as conventional methods often face challenges such as slow processing, limited accuracy, and poor emotional understanding, hindering effective comprehension and automated content interpretation. The proposed framework utilizes a Spatiotemporal Mode Graph-based Temporal Saliency Mapping (SMGTSM) algorithm to enhance frame-wise saliency map generation and support real-time video analysis. SMGTSM achieves a processing time of 523.7 milliseconds, outperforming optical flow and Random Sample Consensus (RANSAC), while maintaining high interpretative accuracy. The system reached 97% accuracy in music feature recognition and 94.67% accuracy, 95.13% recall, and 93.95% F1-score in emotion classification. The result demonstrated that SMGTSM effectively captures subtle spatiotemporal and emotional patterns in video. By leveraging these capabilities, the approach enables real-time interaction, adaptive music interaction, and fosters increased student engagement. The suggested model bridges theoretical instruction with experiential learning by integrating content with video semantics, highlighting the transformative impact of semantic video analysis in music education.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimization of music teaching model based on video image semantic analysis and retrieval

  • 期刊论文

  • 发表日期：

    2025年10月30日

查看全文
  • Lizhe Xu

摘要

Video semantic analysis is essential for interpreting audiovisual content, as conventional methods often face challenges such as slow processing, limited accuracy, and poor emotional understanding, hindering effective comprehension and automated content interpretation. The proposed framework utilizes a Spatiotemporal Mode Graph-based Temporal Saliency Mapping (SMGTSM) algorithm to enhance frame-wise saliency map generation and support real-time video analysis. SMGTSM achieves a processing time of 523.7 milliseconds, outperforming optical flow and Random Sample Consensus (RANSAC), while maintaining high interpretative accuracy. The system reached 97% accuracy in music feature recognition and 94.67% accuracy, 95.13% recall, and 93.95% F1-score in emotion classification. The result demonstrated that SMGTSM effectively captures subtle spatiotemporal and emotional patterns in video. By leveraging these capabilities, the approach enables real-time interaction, adaptive music interaction, and fosters increased student engagement. The suggested model bridges theoretical instruction with experiential learning by integrating content with video semantics, highlighting the transformative impact of semantic video analysis in music education.