Movie Video Summarization- Generating Personalized Summaries Using Spatiotemporal Salient Region Detection

Rajkumar Kannan, Sridhar Swaminathan, Gheorghita Ghinea, Frederic Andres and Kalaiarasi Sonai Muthu Anbananthen
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Rajkumar Kannan: Bishop Heber College, Tiruchirappalli, India
Sridhar Swaminathan: Bennett University, Greater Noida, India
Gheorghita Ghinea: School of Information Systems, Computing and Mathematics, Brunel University, Uxbridge, UK & Norwegian School of Information Technology, Oslo, Norway
Frederic Andres: National Institute of Informatics, Chiyoda City, Japan
Kalaiarasi Sonai Muthu Anbananthen: Multimedia University Malacca, Bukit Beruang, Malaysia

International Journal of Multimedia Data Engineering and Management (IJMDEM), 2019, vol. 10, issue 3, 1-26

Abstract: Video summarization condenses a video by extracting its informative and interesting segments. In this article, a novel video summarization approach is proposed based on spatiotemporal salient region detection. The proposed approach first segments a video into a set of shots which are ranked with spatiotemporal saliency scores. The score for a shot is computed by aggregating the frame level spatiotemporal saliency scores. This approach detects spatial and temporal salient regions separately using different saliency theories related to objects present in a visual scenario. The spatial saliency of a video frame is computed using color contrast and color distribution estimations and center prior integration. The temporal saliency of a video frame is estimated as an integration of local and global temporal saliencies computed using patch level optical flow abstractions. Finally, top ranked shots with the highest saliency scores are selected for generating the video summary. The objective and subjective experimental results demonstrate the efficacy of the proposed approach.

Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jmdem0:v:10:y:2019:i:3:p:1-26

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International Journal of Multimedia Data Engineering and Management (IJMDEM) is currently edited by Chengcui Zhang

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