  Art objects can evoke certain emotions. Color is a fundamental element of
visual art and plays a significant role in how art is perceived. This paper
introduces a novel approach to classifying emotions in art using Fuzzy Sets. We
employ a fuzzy approach because it aligns well with human judgments' imprecise
and subjective nature. Extensive fuzzy colors (n=120) and a broad emotional
spectrum (n=10) allow for a more human-consistent and context-aware exploration
of emotions inherent in paintings. First, we introduce the fuzzy color
representation model. Then, at the fuzzification stage, we process the Wiki Art
Dataset of paintings tagged with emotions, extracting fuzzy dominant colors
linked to specific emotions. This results in fuzzy color distributions for ten
emotions. Finally, we convert them back to a crisp domain, obtaining a
knowledge base of color-emotion associations in primary colors. Our findings
reveal strong associations between specific emotions and colors; for instance,
gratitude strongly correlates with green, brown, and orange. Other noteworthy
associations include brown and anger, orange with shame, yellow with happiness,
and gray with fear. Using these associations and Jaccard similarity, we can
find the emotions in the arbitrary untagged image. We conducted a 2AFC
experiment involving human subjects to evaluate the proposed method. The
average hit rate of 0.77 indicates a significant correlation between the
method's predictions and human perception. The proposed method is simple to
adapt to art painting retrieval systems. The study contributes to the
theoretical understanding of color-emotion associations in art, offering
valuable insights for various practical applications besides art, like
marketing, design, and psychology.
