In this paper, we present a method for implementing a Kansei (sensitivity) operator for an automatic metadata extraction method used for media data. The Kansei operator, which operates on metadata extracted from media data, performs mapping based on human sensitivity by applying a logarithmic function that reflects human sensitivity based on Fechner's law. The proposed method enables metadata that reflects human sensitivity to be extracted according to the Kansei operator for metadata that was obtained according to weighted word categories output by a conventional automatic metadata extraction method. In addition, using a semantic associative search method enables searching to be performed, which matches human intuition for the media data categories. In this paper, we combine the Kansei operator with an automatic metadata extraction method for music data to implement an automatic metadata extraction method having a Kansei operator for music data. In addition, they show the effectiveness of the proposed method experimentally.
ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Hardware and Architecture
- Computational Theory and Mathematics