Visual Attention and Motion Estimation-Based Video Retargeting for Medical Data Security

Qingfang Liu, Baosheng Kang, Qiaozhi Hua*, Zheng Wen, Haipeng Li

*この研究の対応する著者

研究成果: Article査読

抄録

Medical data security is an important guarantee for intelligent medical system. Medical video data can help doctors understand the patients' condition. Medical video retargeting can greatly reduce the storage capacity of data on the premise of preserving the original content information as much as possible. The smaller volume of medical data can reduce the execution time of data encryption and threat detection algorithm and improve the performance of medical data security methods. The existing methods mainly focus on the temporal pixel relationship and foreground motion between adjacent frames, but these methods ignore the user's attention to the video content and the impact of background movement on retargeting, resulting in serious deformation of important content and area. To solve the above problems, this paper proposes an innovative video retargeting method, which is based on visual attention and motion estimation. Firstly, the visual attention map is obtained from eye tracking data, by K-means clustering method and Euclidean distance factor equation. Secondly, the motion estimation map is generated from both the foreground and background displacements, which are calculated based on the feature points and salient object positions between adjacent frames. Then, the visual attention map, the motion estimation map, and gradient map are fused to the importance map. Finally, video retargeting is performed by mesh deformation based on the importance map. Experiment on open datasets shows that the proposed method can protect important area and has a better effect on salient object flutter suppression.

本文言語English
論文番号1343766
ジャーナルSecurity and Communication Networks
2022
DOI
出版ステータスPublished - 2022

ASJC Scopus subject areas

  • 情報システム
  • コンピュータ ネットワークおよび通信

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