Video summarization is applied to reduce redundancy and developa concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes,the...Video summarization is applied to reduce redundancy and developa concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes,the frames that stand out visually are extracted as key frames based on humanattention modeling theories. The schemes for modeling visual attention haveproven to be effective for video summaries. Nevertheless, the high cost ofcomputing in such techniques restricts their usability in everyday situations.In this context, we propose a method based on KFE (key frame extraction)technique, which is recommended based on an efficient and accurate visualattention model. The calculation effort is minimized by utilizing dynamicvisual highlighting based on the temporal gradient instead of the traditionaloptical flow techniques. In addition, an efficient technique using a discretecosine transformation is utilized for the static visual salience. The dynamic andstatic visual attention metrics are merged by means of a non-linear weightedfusion technique. Results of the system are compared with some existing stateof-the-art techniques for the betterment of accuracy. The experimental resultsof our proposed model indicate the efficiency and high standard in terms ofthe key frames extraction as output.展开更多
基金This work was supported in part by Qatar National Library,Doha,Qatar,and in part by the Qatar University Internal under Grant IRCC-2021-010。
文摘Video summarization is applied to reduce redundancy and developa concise representation of key frames in the video, more recently, video summaries have been used through visual attention modeling. In these schemes,the frames that stand out visually are extracted as key frames based on humanattention modeling theories. The schemes for modeling visual attention haveproven to be effective for video summaries. Nevertheless, the high cost ofcomputing in such techniques restricts their usability in everyday situations.In this context, we propose a method based on KFE (key frame extraction)technique, which is recommended based on an efficient and accurate visualattention model. The calculation effort is minimized by utilizing dynamicvisual highlighting based on the temporal gradient instead of the traditionaloptical flow techniques. In addition, an efficient technique using a discretecosine transformation is utilized for the static visual salience. The dynamic andstatic visual attention metrics are merged by means of a non-linear weightedfusion technique. Results of the system are compared with some existing stateof-the-art techniques for the betterment of accuracy. The experimental resultsof our proposed model indicate the efficiency and high standard in terms ofthe key frames extraction as output.
