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As video content continues to expand, effective retrieval methods must account for the complexity of diverse data types and contexts. Traditional video retrieval systems often prioritize prominent features, overlooking valuable details in less noticeable regions, such as the periphery of video frames. This can result in incomplete retrieval, especially when subtle yet important information is present in these areas. In this paper, we introduce a novel context-aware video retrieval technique that emphasizes peripheral information, detecting and extracting features from these minor-impact regions. By focusing on such overlooked details, our approach enhances both the accuracy and contextual relevance of retrieval. Extensive testing validates the method’s effectiveness, demonstrating its potential for real-world applications where detailed and precise video retrieval is crucial.

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Unveiling Peripheral Information: A Context-Aware Video Retrieval Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Quan Nguyen Hung,
  • Nhan Nguyen Viet Thien,
  • Hoang Tran Van,
  • An Tran Thuy,
  • Dat Phan Thanh,
  • Thuyen Tran Doan,
  • Khiem Le,
  • Tien Do,
  • Duy-Dinh Le,
  • Thanh Duc Ngo

摘要

As video content continues to expand, effective retrieval methods must account for the complexity of diverse data types and contexts. Traditional video retrieval systems often prioritize prominent features, overlooking valuable details in less noticeable regions, such as the periphery of video frames. This can result in incomplete retrieval, especially when subtle yet important information is present in these areas. In this paper, we introduce a novel context-aware video retrieval technique that emphasizes peripheral information, detecting and extracting features from these minor-impact regions. By focusing on such overlooked details, our approach enhances both the accuracy and contextual relevance of retrieval. Extensive testing validates the method’s effectiveness, demonstrating its potential for real-world applications where detailed and precise video retrieval is crucial.