Computer Science ›› 2021, Vol. 48 ›› Issue (4): 117-122.doi: 10.11896/jsjkx.200800160

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Video Character Relation Extraction Based on Multi-feature Fusion and Fine-granularity Analysis

LYU Jin-na, XING Chun-yu , LI Li   

  1. School of Information Management,Beijing Information Science & Technology University,Beijing 100192,China
  • Received:2020-06-24 Revised:2020-10-16 Online:2021-04-15 Published:2021-04-09
  • About author:LYU Jin-Na,born in 1981,Ph.D,lecture,is a member of China Computer Federation.Her main research interests include multimedia content analysis,social network analysis and so on.
  • Supported by:
    Beijing University of Information Technology Foundation(2035012).

Abstract: Video character relation extraction is an important task of information extraction.It is valuable for video description,video retrieval,character search,public security supervision,etc.Due to the huge gap between the underlying pixels of video data and the semantics of high-level relation,it is difficult to accurately extract the relations.Most existing studies are based on coarse- granularity analysis,such as co-occurrence of characters,which ignores the fine-granularity information.In order to solve the problem that it is difficult to accurately and completely extract the relations among video characters,this paper proposes a new method for extracting relations of video characters based on multi-feature fusion and fine-granularity analysis.First,a new character entity recognition model,named CRMF(Character Recognition based on Multi-feature Fusion),is proposed.Through this manner,we can generate a more complete character set using face and body features fusion.Second,we exploit a character relationship recognition model based on fine-granularity features,named FGAG(Fine-Granularity Analysis based on GCN),which not only fuses the spatio-temporal features,but also considers the fine-granularity objects information related to the characters.Thus a better mapping can be established to accurately identify the character relations.Comprehensive evaluations are conducted on the movie video and SRIV character relationship recognition dataset,and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on character entity and relation recognition,F1 value increases by 14.4% and accuracy increases by 10.1%.

Key words: Character relation recognition, Deep learning, Multi-feature fusion, Relation extraction, Video analysis

CLC Number: 

  • TP391
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