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Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video. Movie summarization condenses a full-length movie into a summary that still retains the most significant and interesting content of the original movie. In the past, several movie summarization systems have been proposed to generate a movie summary based on objective video summarization criteria such as audio, visual, textual saliencies, user feedback or responses, and character analysis. Nevertheless, a generic summary, which is common to everyone and…mehr

Produktbeschreibung
Video summarization aims at producing a compact version of a full-length video while preserving the significant content of the original video. Movie summarization condenses a full-length movie into a summary that still retains the most significant and interesting content of the original movie. In the past, several movie summarization systems have been proposed to generate a movie summary based on objective video summarization criteria such as audio, visual, textual saliencies, user feedback or responses, and character analysis. Nevertheless, a generic summary, which is common to everyone and produced based only on an objective summarization criterion will not satisfy every user, as their preferences for the summary differ vastly for the same movie. This book presents a novel personalized movie summarization system based on users' preferences as well as their interests and state of the art spatio-temporal salient region detection methods for users.
Autorenporträt
Sridhar Swaminathan is AI Team Lead at Karomi Technology, Chennai. Previously, he worked at L&T EduTech, Chennai; Bennett University, Noida and Multimedia University, Malaysia.Rajkumar Kannan is Dean (IR), Professor of Data Science and Computer Science at Bishop Heber College, India. Previously he worked for King Faisal University, Saudi Arabia.