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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 <jats:italic>dynamic<\/jats:italic> character graph that captures the temporal evolution of character interaction. We refer to this as the <jats:italic>character interaction graph<\/jats:italic> (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.<\/jats:p>","DOI":"10.1007\/s11042-020-09449-6","type":"journal-article","created":{"date-parts":[[2020,8,31]],"date-time":"2020-08-31T15:04:03Z","timestamp":1598886243000},"page":"33103-33118","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Dynamic character graph via online face clustering for movie analysis"],"prefix":"10.1007","volume":"79","author":[{"given":"Prakhar","family":"Kulshreshtha","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tanaya","family":"Guha","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,8,31]]},"reference":[{"issue":"11","key":"9449_CR1","doi-asserted-by":"publisher","first-page":"4381","DOI":"10.1109\/TIP.2015.2463223","volume":"24","author":"X Cao","year":"2015","unstructured":"Cao X, Zhang C, Zhou C, Fu H, Foroosh H (2015) Constrained multi-view video face clustering. 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