TY - JOUR
T1 - The logical style painting classifier based on Horn clauses and explanations (ℓ-SHE)
AU - Costa, Vicent
AU - Dellunde, Pilar
AU - Falomir, Zoe
N1 - Publisher Copyright:
© 2019 The Author(s).
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
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PY - 2019/11/21
Y1 - 2019/11/21
N2 - 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.
AB - 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.
KW - Horn clause
KW - art
KW - classifier
KW - explainable AI
KW - fuzzy logics
KW - logic programming
KW - qualitative colour
UR - http://www.scopus.com/inward/record.url?scp=85081676355&partnerID=8YFLogxK
UR - https://www.mendeley.com/catalogue/26d82115-6f86-3dae-aeee-20bcbb075d1b/
U2 - 10.1093/jigpal/jzz029
DO - 10.1093/jigpal/jzz029
M3 - Article
VL - 29
SP - 96
EP - 119
IS - 1
M1 - 1
ER -