Multi-Dimensional Semantic Computing with Spatial-Temporal and Semantic Axes for Multi-spectrum Images in Environment Analysis

Yasushi Kiyoki, Xing Chen, Shiori Sasaki, Chawan Koopipat

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Semantic computing is an important and promising approach to semantic analysis for various environmental phenomena and changes in real world. This paper presents a new semantic computing method with multi-spectrum images for analyzing and interpreting environmental phenomena and changes occurring in the physical world. We have already presented a concept of 'Semantic Computing System' for realizing global environmental analysis. This paper presents a new semantic computing method to realize semantic associative search for the multiple-colours-spectrum images in the multi-dimensional semantic space, that is 'multi-spectrum semantic-image space' with semantic projection functions. This space is created for dynamically computing semantic equivalence, similarity and difference between multi-spectrum images and environmental situations. We apply this system to global environmental analysis as a new platform of environmental computing. We have already presented the 5D World Map System, as an international research environment with spatio-temporal and semantic analysers. We also present several new approaches to global environmental-analysis for multi-spectrum images in 'multi-spectrum semantic-image space.'.

Original languageEnglish
Title of host publicationInformation Modelling and Knowledge Bases XXVII
PublisherIOS Press
Pages14-30
Number of pages17
Volume280
ISBN (Print)9781614996101
DOIs
Publication statusPublished - 2016

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume280
ISSN (Print)09226389

ASJC Scopus subject areas

  • Artificial Intelligence

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    Kiyoki, Y., Chen, X., Sasaki, S., & Koopipat, C. (2016). Multi-Dimensional Semantic Computing with Spatial-Temporal and Semantic Axes for Multi-spectrum Images in Environment Analysis. In Information Modelling and Knowledge Bases XXVII (Vol. 280, pp. 14-30). (Frontiers in Artificial Intelligence and Applications; Vol. 280). IOS Press. https://doi.org/10.3233/978-1-61499-611-8-14