Style Painting Classifier Based on Horn Clauses and Explanations (SHE)

Vicent Costa*, Pilar Dellunde, Zoe Falomir

*Corresponding author for this work

Research output: Chapter in BookChapterResearchpeer-review

1 Citation (Scopus)


This paper presents an style painting classifier, based on Horn clauses in rational Pavelka logic and qualitative colour descriptors, that provides explanations (SHE). A fuzzy representation of colour traits for Baroque, Impressionism and Post-Impressionism art styles is introduced. The SHE-algorithm has been implemented in Swi-Prolog and tested on the 90 paintings in QArt-Dataset. the Baroque style was classified in 90% of accuracy. The general accuracy for all the art styles was 73.3%. SHE provided explanations of right classifications, and also of outliers by giving a second option depending on the membership degree of the painting to a certain style.

Original languageEnglish
Title of host publicationArtificial Intelligence Research and Development
Subtitle of host publicationCurrent Challenges, New Trends and Applications
EditorsKarina Gibert, Zoe Falomir, Enric Plaza
PublisherIOS Press
Number of pages10
ISBN (Print)9781614999171
Publication statusPublished - 2018

Publication series

NameFrontiers in Artificial Intelligence and Applications
ISSN (Print)0922-6389


  • art
  • classifier
  • explainable AI
  • Horn clause
  • logic programming
  • qualitative colour


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