DOI: 10.4018/IJMDEM.2019070101 International Journal of Multimedia Data Engineering and Management Volume 10 • Issue 3 • July-September 2019 Copyright © 2019, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 1 Movie Video Summarization- Generating Personalized Summaries Using Spatiotemporal Salient Region Detection Rajkumar Kannan, Bishop Heber College, Tiruchirappalli, India https://orcid.org/0000-0002-7812-8595 Sridhar Swaminathan, Bennett University, Greater Noida, India Gheorghita Ghinea, Brunel University, Uxbridge, UK Frederic Andres, National Institute of Informatics, Chiyoda City, Japan https://orcid.org/0000-0002-5003-7579 Kalaiarasi Sonai Muthu Anbananthen, Multimedia University Malacca, Bukit Beruang, Malaysia 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. KEywoRDS Salient Region Detection, Spatial Saliency, Spatiotemporal Saliency, Temporal Saliency, Video Summarization, Visual Attention Modeling 1. INTRoDUCTIoN In this digital era, rapid growth of videos at exponential rate necessitates development of digital assistive technologies in accessing the voluminous video content (Money, & Agius, 2008). Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video (Kannan et al., 2015). Video summarization assists the users in understanding the overall content of a video quickly and helps them decide whether to watch the entire video or not. Generally, video summaries are visualized using either keyframes or video skims (Ejaz et al., 2014). The keyframes generated by a video summarization approach comprise a collection of video