Viewer’s Affective Feedback for Video Summarization
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For different reasons, many viewers like to watch a summary of films without having to waste their time. Traditionally, video film was analyzed manually to provide a summary of it, but this costs an importantamount of work time. Therefore, it has become urgent to propose a tool for the automatic videosummarization job. The automatic video summarization aims at extracting all of the important moments inwhich viewers might be interested. All summarization criteria can differ from one video to another. Thispaper presents how the emotional dimensions issued from real viewers can be used as an important input forcomputing which part is the most interesting in the total time of a film. Our results, which are based on labexperiments that were carried out, are significant and promising.
Abstract
For different reasons, many viewers like to watch a summary of films without having to waste their time. Traditionally, video film was analyzed manually to provide a summary of it, but this costs an importantamount of work time. Therefore, it has become urgent to propose a tool for the automatic videosummarization job. The automatic video summarization aims at extracting all of the important moments inwhich viewers might be interested. All summarization criteria can differ from one video to another. Thispaper presents how the emotional dimensions issued from real viewers can be used as an important input forcomputing which part is the most interesting in the total time of a film. Our results, which are based on labexperiments that were carried out, are significant and promising.
참고문헌 (24)
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