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- W3082103684 abstract "Abstract An effective approach to automated movie content analysis involves building a network (graph) of its characters. Existing work usually builds a static character graph to summarize the content using metadata, scripts or manual annotations. We propose an unsupervised approach to building a dynamic character graph that captures the temporal evolution of character interaction. We refer to this as the character interaction graph (CIG). Our approach has two components: (i) an online face clustering algorithm that discovers the characters in the video stream as they appear, and (ii) simultaneous creation of a CIG using the temporal dynamics of the resulting clusters. We demonstrate the usefulness of the CIG for two movie analysis tasks: narrative structure (acts) segmentation and major character retrieval. Our evaluation on full-length movies containing more than 5000 face tracks shows that the proposed approach achieves superior performance for both the tasks." @default.
- W3082103684 created "2020-09-08" @default.
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- W3082103684 date "2020-08-31" @default.
- W3082103684 modified "2023-10-18" @default.
- W3082103684 title "Dynamic character graph via online face clustering for movie analysis" @default.
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- W3082103684 doi "https://doi.org/10.1007/s11042-020-09449-6" @default.
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