Scholarlink

<p>Video classification is an important domain within computer vision. It categorizes video content into meaningful classes such as actions or emotional states. In relation to image classification, it has to deal with the problem of spatiotemporal dimensions as well as a large data volume that is present in a video. In this work we introduce a novel distance metric based video summarization technique which minimizes the size of the dataset while maintaining key temporal information. We performed our experiments using distance metrics such as norm of rows distance other than euclidean distance, norm of columns distance and eigenvalue based distance metrics. Our results show that the norm of rows distance performed well and provides a suitable balance between efficiency and accuracy. Our proposed method achieved significant accuracy of <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(81.23\%\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <EquationSource Format="TEX">\(92.42\%\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq3"> <EquationSource Format="TEX">\(98.89\%\)</EquationSource> </InlineEquation> and <InlineEquation ID="IEq4"> <EquationSource Format="TEX">\(90.27\%\)</EquationSource> </InlineEquation> on MMAC, UCF101, UCF11 and HMDB51 benchmark datasets. Our proposed technique continuously tracked temporal information while recalculating the distance from each key frame. Due to less computational demands, our approach performs effectively in real-world application scenarios.</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

Innovative temporal summarization for complex video classification

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月9日

查看全文
  • Adnan Khan,
  • Alireza Rahnama,
  • Ashhadul Islam,
  • Samir Brahim Belhaouari

摘要

Video classification is an important domain within computer vision. It categorizes video content into meaningful classes such as actions or emotional states. In relation to image classification, it has to deal with the problem of spatiotemporal dimensions as well as a large data volume that is present in a video. In this work we introduce a novel distance metric based video summarization technique which minimizes the size of the dataset while maintaining key temporal information. We performed our experiments using distance metrics such as norm of rows distance other than euclidean distance, norm of columns distance and eigenvalue based distance metrics. Our results show that the norm of rows distance performed well and provides a suitable balance between efficiency and accuracy. Our proposed method achieved significant accuracy of \(81.23\%\) , \(92.42\%\) , \(98.89\%\) and \(90.27\%\) on MMAC, UCF101, UCF11 and HMDB51 benchmark datasets. Our proposed technique continuously tracked temporal information while recalculating the distance from each key frame. Due to less computational demands, our approach performs effectively in real-world application scenarios.