Identifying turning points in animated cartoons. (1st June 2019)

• Record Type: Journal Article
• Authors: Liu, Chang; Last, Mark; Shmilovici, Armin
• Is Part Of: Expert systems with applications. Volume 123(2019)
• Page Start: 246
• Page End: 255
• Publication Date: 2019-06-01
• Subjects: Story's turning points -- Story elements detection -- Story understanding -- Video analytics; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2019.01.003
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: This paper proposes a methodology for turning points detection in movies. The methodology builds upon drifts between the event clock and the weighted clock. Only the movie subtitles are used as input. Encouraging results are obtained on 28 episodes of a popular cartoon series. The methodology is capable of discovering additional story elements in a movie. Abstract: Detecting key story elements such as protagonist, opponent, desire, turning points, battle, and victory, etc. is essential for various narrative work applications including content retrieval and content recommendation systems. The task of automatically identifying story elements is challenging because of its complexity and subjectiveness and currently, there are no available algorithms for this task. In this paper, we focus on identifying turning points in a story of a cartoon movie. The proposed methodology extends the novel two-clocks theory, originally validated on scripts of theatre plays, to video stories. The assumption behind the two-clocks theory is that the perception of time is different when some special event happens to a certain agent (e.g., time flows slower for a patient and quicker for a tourist). The story timeline is monitored with two clocks: an event clock, which measures the regular time flow of the story; and a weighted clock, which measures the timing of the story events. We have conducted an experiment on 28 episodes of a cartoon series and achieved promising results: 78.6% precision for turning points identification and 100% precision for key scene detection. The proposed approach is the first step towards development of intelligent systems for automated understanding of stories in narrative works such as cinema movies and even amateur videos uploaded to the Internet.