A ‘Kansei’ multimedia and semantic computing system for cross-cultural communication

Shlomo Dubnov, Kevin Burns, Yasushi Kiyoki

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

In the design of multimedia computing systems, one of the most important issues is how to search and analyze media data (images, music, movies and documents), according to user’s impressions and contexts. This paper presents “Kansei-Multimedia Computing System” for realizing international and cross-cultural research environments, as a new platform of multimedia computing system. We introduce a “Kansei” and semantic associative search method based on the “Mathematical Model of Meaning (MMM)”. The concept of “Kansei” includes several meanings on sensitive recognition, such as “emotion”, “impression”, “human senses”, “feelings”, “sensitivity”, “psychological reaction” and “physiological reaction”. MMM realizes “Kansei” processing and semantic associative search for media data, according to user’s impressions and contexts. This model is applied to compute semantic correlations between keywords, images, music, movies and documents dynamically in a context-dependent way. This system based on MMM realizes (1) “Kansei” image and music search and analysis for cooperative creation and manipulation of multimedia objects and (2) Cross-cultural communications with music and images databases.

Original languageEnglish
Title of host publicationSpringerBriefs in Computer Science
PublisherSpringer
Pages1-20
Number of pages20
Edition9783319428710
DOIs
Publication statusPublished - 2016 Jan 1

Publication series

NameSpringerBriefs in Computer Science
Number9783319428710
ISSN (Print)2191-5768
ISSN (Electronic)2191-5776

Keywords

  • Multimedia database
  • Music database
  • Music piece
  • Semantic correlation
  • Semantic space

ASJC Scopus subject areas

  • Computer Science(all)

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  • Cite this

    Dubnov, S., Burns, K., & Kiyoki, Y. (2016). A ‘Kansei’ multimedia and semantic computing system for cross-cultural communication. In SpringerBriefs in Computer Science (9783319428710 ed., pp. 1-20). (SpringerBriefs in Computer Science; No. 9783319428710). Springer. https://doi.org/10.1007/978-3-319-42873-4_1