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Application of computational media aesthetics methodology to extracting color semantics in film

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Published:01 December 2002Publication History

ABSTRACT

Using film grammar as the underpinning, we study the extraction of structures in video based on color using a wide configuration of clustering methods combined with existing and new similarity measures. We study the visualisation of these structures, which we call Scene-Cluster Temporal Charts and show how it can bring out the interweaving of different themes and settings in a film. We also extract color events that filmmakers use to draw/force a viewer's attention to a shot/scene. This is done by first extracting a set of colors used rarely in film, and then building a probabilistic model for color event detection. We demonstrate with experimental results from ten movies that our algorithms are effective in the extraction of both scene-cluster temporal charts and color events.

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  1. Application of computational media aesthetics methodology to extracting color semantics in film

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      cover image ACM Conferences
      MULTIMEDIA '02: Proceedings of the tenth ACM international conference on Multimedia
      December 2002
      683 pages
      ISBN:158113620X
      DOI:10.1145/641007

      Copyright © 2002 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 1 December 2002

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      MULTIMEDIA '02 Paper Acceptance Rate46of330submissions,14%Overall Acceptance Rate995of4,171submissions,24%

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