Can Humans and Machines Classify Photographs as Depicting Negation?

Yuri Sato, Koji Mineshima

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

Abstract

How logical concepts such as negation can be visually represented is of central importance in the study of diagrammatic reasoning. To explore various ways in which negation can be visually represented, this study focuses on photographs as instances of purely visual representations. We use real-world photographic image data and study how well humans can classify those images as depicting negation. We also compare the human performance with a state-of-the-art machine (deep) learning model on this classification task. The present paper gives some preliminary results on our data-driven analyses.

Original languageEnglish
Title of host publicationDiagrammatic Representation and Inference - 12th International Conference, Diagrams 2021, Proceedings
EditorsAmrita Basu, Gem Stapleton, Sven Linker, Catherine Legg, Emmanuel Manalo, Petrucio Viana
PublisherSpringer Science and Business Media Deutschland GmbH
Pages348-352
Number of pages5
ISBN (Print)9783030860615
DOIs
Publication statusPublished - 2021
Event12th International Conference on the Theory and Application of Diagrams, Diagrams 2021 - Virtual, Online
Duration: 2021 Sep 282021 Sep 30

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12909 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on the Theory and Application of Diagrams, Diagrams 2021
CityVirtual, Online
Period21/9/2821/9/30

Keywords

  • Cognitive science
  • Machine learning
  • Negation
  • Photograph

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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