Predicting Emotion From Color Present in Images and Video Excerpts by Machine Learning

Wedolowska, A.; Weber, Dawid; Kostek, Bożena

doi:10.1109/access.2023.3289713

Cited by 1 publication

(3 citation statements)

References 37 publications

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“…For example, sadness is represented mostly with black and brown, and blue color is relevant to fear. Similar results were also received in study [20], where the authors proposed a machine learning model. One of the most significant findings is that happiness is undoubtedly associated with yellow and orange, as indicated in both the model prediction outcomes and psychological studies.…”

Section: Discussionsupporting

confidence: 82%

“…Several recent studies have explored the emotional effects of art objects on humans [73], [4], [20], [22]. Conceptually similar works were completed by psychologists via surveys and case studies among real people.…”

Section: Discussionmentioning

confidence: 99%

“…The authors of the other study designed a machine-learning algorithm for emotion classification in images, utilizing color information and categorizing emotions into three distinct classes: negative, positive, and neutral [20]. Another study proposed the L-EEP method, utilizing Culture Technology, to automatically extract eight emotions from Iranian-Islamic paintings through color analysis and the automated Luscher test [21].…”

Section: Related Work a Color-emotion Association Studiesmentioning

confidence: 99%

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Color-Emotion Associations in Art: Fuzzy Approach

Muratbekova,

Shamoi

2024

IEEE Access

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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.INDEX TERMS Fuzzy sets, emotions in art, color palette, classification, color-emotion model, art image analysis, color perception, image processing emotion detection.

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Section: Discussionsupporting

confidence: 82%