What do you wish to see? A summarization system for movies based on user preferences

Abstract

Abstract. 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 low-level video features such as color, motion, texture, etc. However, a generic summary, which is common to everyone and is produced based only on low-level video features will not satisfy every user. As users' preferences for the summary differ vastly for the same movie, there is a need for a personalized movie summarization system nowadays. To address this demand, this paper proposes a novel system to generate semantically meaningful video summaries for the same movie, which are tailored to the preferences and interests of a user. For a given movie, shots and scenes are automatically detected and their high-level features are semi-automatically annotated. Preferences over high-level movie features are explicitly collected from the user using a query interface. The user preferences are generated by means of a stored-query. Movie summaries are generated at shot level and scene level, where shots or scenes are selected for summary skim based on the similarity measured between shots and scenes, and the user's preferences. The proposed movie summarization system is evaluated subjectively using a sample of 20 subjects with eight movies in the English language. The quality of the generated summaries is assessed by informativeness, enjoyability, relevance, and acceptance metrics and Quality of Perception measures. Further, the usability of the proposed summarization system is subjectively evaluated by conducting a questionnaire survey. The experimental results on the performance of the proposed movie summarization approach show the potential of the proposed system.

Using social media to explore regional cuisine preferences in China

“…The results show that the estimated political preferences of media outlets' audiences reflect the liberal or conservative learnings of the media outlets. Kannan et al (2015) presented a novel, preference-aware, movie-summarization system to obtain semantically meaningful personalized movie summaries according to a user's interests. Zhao et al (2015) proposed a probabilistic model combining opinion mining and geographical modeling to capture both topical-region preference and category aware topicalaspect preference.…”

Video Skimming: Taxonomy and Comprehensive Survey

“…[ 19,32,33,39,74,135,145,169,173,203] Surveillance Continuous recording of activity using either a static or moving camera. Single or multiple cameras Event detection and anomaly identification.…”

“…The personalization can also be in terms of specific features of the video like people, objects, events etc. [74], [181] or it can be in terms of viewing time. The preference can also be given Fig.…”

“…Camera movement based semantics can be determined [17] for sports summarization. [74] identifies a variety of concepts such as a beach, people, flowers, indoors, etc., for ranking the frames and [27] provides for personalization by adapting summary to viewer preferences of concepts. The works in [16,98,111,169] not only identify the concepts but also model the interactions between the prominent entities (any predefined person or object) for determining the representative ones.…”

Personalized Movie Summarization Using Deep CNN‐Assisted Facial Expression Recognition

“…In recent years, many MS techniques have been presented by researchers that can be broadly categorized into automatic MS techniques [1][2][3][4][5][6][7][8][9][10] and user-preference based MS techniques [11][12][13][14][15]. In automatic MS techniques, there is no direct preference from the users to generate a summary.…”