Cross-Cultural Multimedia Computing with Impression-Based Semantic Spaces

Abstract

Over the past decade, the rapid progress of multimedia data management technology has realized the large scale of media data transfer and resource-accumulation in the world. The multimedia computing technology has also been creating new information provision environments in the world-wide scope. Innovative integrations of large scale multimedia data management and computing technology will lead to a new information society. In the design of multimedia systems, one of the most important issues is how to search and analyze media data (images, music, movies and documents), according to impressions and contexts. We have proposed and introduced a Kansei and semantic associative search method based on our Mathematical Model of Meaning (MMM) [11], [13], [14]. The concept of Kansei includes several meanings on sensitive recognition, such as impression, human senses, feelings, sensitivity, psychological reaction and physiological reaction. This model realizes Kansei processing and semantic associative search for media according to user's impressions and contexts. This model is applied to compute semantic correlations between keywords, images, music and documents dynamically in a context-dependent way. The main feature of this model is to realize semantic associative search in the 2000 dimensional orthogonal semantic space with semantic projection functions. This space is created for dynamically computing semantic equivalence or similarity between keywords and media data. We have constructed Cross-Cultural Multimedia Computing Systems for sharing and analyzing different cultures with semantic associative functions applied to & multimedia data, as a new platform of cross-cultural collaborative environments. This environment enables to create a remote, interactive and real-time cultural and academic research exchange among different countries and cultures.