Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie summarization method to capture the semantic content in...Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie summarization method to capture the semantic content in movies based on a string of IE-Role Nets. An IE-Role Net(interaction and emotion rolenet) models the emotion and interactions of roles in a shot of the movie. The whole movie is represented as a string of IE-Role Nets. Summarization of a movie is transformed into finding an optimal substring with user-specified summarization ratio.Hierarchical substring mining is conducted to find an optimal substring of the whole movie. We have conducted objective and subjective experiments on our method. Experimental results show the ability of our method to capture the semantic content of movies.展开更多
基金supported by the National Basic Research Program of China under Grant No.2011CB302200-Gthe National Natural Science Foundation of China under Grant Nos. 61370074 and 61402091the Fundamental Research Funds for the Central Universities of China under Grant Nos. N120404007 and N140404012
文摘Roles, their emotion, and interactions between them are three key elements for semantic content understanding of movies. In this paper, we proposed a novel movie summarization method to capture the semantic content in movies based on a string of IE-Role Nets. An IE-Role Net(interaction and emotion rolenet) models the emotion and interactions of roles in a shot of the movie. The whole movie is represented as a string of IE-Role Nets. Summarization of a movie is transformed into finding an optimal substring with user-specified summarization ratio.Hierarchical substring mining is conducted to find an optimal substring of the whole movie. We have conducted objective and subjective experiments on our method. Experimental results show the ability of our method to capture the semantic content of movies.
