Multiple instance learning for labeling faces in broadcasting news video
Proceedings of the 13th annual ACM international conference on Multimedia
Logotype detection to support semantic-based video annotation
Image Communication
Semantic event representation and recognition using syntactic attribute graph grammar
Pattern Recognition Letters
Taking the bite out of automated naming of characters in TV video
Image and Vision Computing
Social network analysis in a movie using character-net
Multimedia Tools and Applications
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Managing the video content for searching and summarizing has become a challenging task. Extracting semantics from video scenes enables information to be presented in a more understandable manner. Finding the semantics between video contexts is a difficult task; much recent research has focused on this issue. Most videos, such as TV serials and commercial movies, are character- centric. Therefore, the context and relationship between characters needs to be organized systematically to analyze the video. So, it is necessary to identify the contextual relationships between characters in the scene and the video. We propose Character-Net, a network structure. It finds characters in a group of shots, extracts the speaker and listeners in the scene, represents it with character-based graphs and draws the relationship between all characters by accumulating the character-based graphs at video. In this paper, we describe how to build Character-Net. Experimental results show Character-Net is an effective methodology to extract the major characters in videos.