The logical style painting classifier based on Horn clauses and explanations (ℓ-SHE)

Vicent Costa, Pilar Dellunde, Zoe Falomir

Research output: Contribution to journalArticleResearchpeer-review

2 Citations (Scopus)

Abstract

This paper presents a logical Style painting classifier based on evaluated Horn clauses, qualitative colour descriptors and Explanations (ℓ-SHE). Three versions of ℓ-SHE are defined, using rational Pavelka logic (RPL), and expansions of Gödel logic and product logic with rational constants: RPL, G(Q) and ∩ (Q), respectively. We introduce a fuzzy representation of the more representative colour traits for the Baroque, the Impressionism and the Post-Impressionism art styles. The ℓ-SHE algorithm has been implemented in Swi-Prolog and tested on 90 paintings of the QArt-Dataset and on 247 paintings of the Paintings-91-PIB dataset. The percentages of accuracy obtained in the QArt-Dataset for each ℓ-SHE version are 73.3% (RPL), 65.6% (G(Q)) and 68.9% (∩ (Q)). Regarding the Paintings-91-PIB dataset, the percentages of accuracy obtained for each ℓ-SHE version are 60.2% (RPL), 48.2% (G(Q)) and 57.0% (∩ (Q)). Our logic definition for the Baroque style has obtained the highest accuracy in both datasets, for all the ℓ-SHE versions (the lowest Baroque case gets 85.6% of accuracy). An important feature of the classifier is that it provides reasons regarding why a painting belongs to a certain style. The classifier also provides reasons about why outliers of one art style may belong to another art style, giving a second classification option depending on its membership degrees to these styles.

Original languageEnglish
Pages (from-to)96-119
Number of pages24
JournalLogic Journal of the IGPL
Volume29
Issue number1
DOIs
Publication statusPublished - 1 Feb 2021

Keywords

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

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