Identifying Emotions Aroused from Paintings

Lu, Xin; Sawant, Neela; Newman, Michelle G.; Adams, Reginald B.; Wang, James; Li, Jia

doi:10.1007/978-3-319-46604-0_4

Cited by 10 publications

(7 citation statements)

References 20 publications

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“…Furthermore, dominance will not be used since the images do not present relevant information about this variable. Therefore, in line with this author (Lu et al, 2016), if valence is above the median (4.33) the image is categorized as positive, and in the contrary case, as negative (Figure 4).…”

Section: Methodssupporting

confidence: 62%

“…OASIS dataset is composed of 900 photographs, which are categorized into measurements of arousal, valence, and dominance (DES scale). Our methodology uses the proposal of Lu et al (2016) which is based solely on the valence descriptor since it allows for defining emotion between two states: positive and negative. Arousal is thus unnecessary since it only defines the emotional intensity.…”

Section: Step (Ii) Feature Extractionmentioning

confidence: 99%

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Detection of Emotions in Artworks Using a Convolutional Neural Network Trained on Non-Artistic Images: A Methodology to Reduce the Cross-Depiction Problem

González-Martín

Carrasco

Wielandt

2023

Empirical Studies of the Arts

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This research is framed within the study of automatic recognition of emotions in artworks, proposing a methodology to improve performance in detecting emotions when a network is trained with an image type different from the entry type, which is known as the cross-depiction problem. To achieve this, we used the QuickShift algorithm, which simplifies images’ resources, and applied it to the Open Affective Standardized Image (OASIS) dataset as well as the WikiArt Emotion dataset. Both datasets are also unified under a binary emotional system. Subsequently, a model was trained based on a convolutional neural network using OASIS as a learning base, in order to then be applied on the WikiArt Emotion dataset. The results show an improvement in the general prediction performance when applying QuickShift (73% overall). However, we can observe that artistic style influences the results, with minimalist art being incompatible with the methodology proposed.

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Section: Methodssupporting

confidence: 62%

Section: Step (Ii) Feature Extractionmentioning

confidence: 99%

Detection of Emotions in Artworks Using a Convolutional Neural Network Trained on Non-Artistic Images: A Methodology to Reduce the Cross-Depiction Problem

González-Martín

Carrasco

Wielandt

2023

Empirical Studies of the Arts

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“…However, such approaches resulted in high levels of multicollinearity between emotions, making it difficult to disentangle emotions using traditional regression models. In contrast, adopting a dimensional approach not only aligns well with emerging theoretical accounts of emotion but has been validated by James Wang and his colleagues in the successful assessment of human aesthetics and emotions evoked by visual scenes, as well as bodily expressed emotion [ 46 ], [ 47 ], [ 48 ], [ 49 ], [ 50 ], [ 51 ], [ 52 ]. This offers a methodological approach that is consistent with dimensional theories of emotion.…”

Section: Emotion: the Psychological Foundationmentioning

confidence: 99%

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

Wang

Zhao

et al. 2023

Proc. IEEE

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The emergence of artificial emotional intelligence technology is revolutionizing the fields of computers and robotics, allowing for a new level of communication and understanding of human behavior that was once thought impossible. While recent advancements in deep learning have transformed the field of computer vision, automated understanding of evoked or expressed emotions in visual media remains in its infancy. This foundering stems from the absence of a universally accepted definition of “emotion,” coupled with the inherently subjective nature of emotions and their intricate nuances. In this article, we provide a comprehensive, multidisciplinary overview of the field of emotion analysis in visual media, drawing on insights from psychology, engineering, and the arts. We begin by exploring the psychological foundations of emotion and the computational principles that underpin the understanding of emotions from images and videos. We then review the latest research and systems within the field, accentuating the most promising approaches. We also discuss the current technological challenges and limitations of emotion analysis, underscoring the necessity for continued investigation and innovation. We contend that this represents a “Holy Grail” research problem in computing and delineate pivotal directions for future inquiry. Finally, we examine the ethical ramifications of emotion-understanding technologies and contemplate their potential societal impacts. Overall, this article endeavors to equip readers with a deeper understanding of the domain of emotion analysis in visual media and to inspire further research and development in this captivating and rapidly evolving field.

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“…This work well imitates the process of inferring emotion from art paintings. To tackle the scarcity of well-labeled paintings, Lu et al [118] proposed an adaptive learning strategy to use the labeled photographs and unlabeled paintings to identify the emotions of paintings. The differences between the two types of images are considered in the learning process.…”

Section: Datasetmentioning

confidence: 99%

Affective Image Content Analysis: Two Decades Review and New Perspectives

Zhao

Yao

Yang

et al. 2022

IEEE Trans. Pattern Anal. Mach. Intell.

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Images can convey rich semantics and induce various emotions in viewers. Recently, with the rapid advancement of emotional intelligence and the explosive growth of visual data, extensive research efforts have been dedicated to affective image content analysis (AICA). In this survey, we will comprehensively review the development of AICA in the recent two decades, especially focusing on the state-of-the-art methods with respect to three main challenges -the affective gap, perception subjectivity, and label noise and absence. We begin with an introduction to the key emotion representation models that have been widely employed in AICA and description of available datasets for performing evaluation with quantitative comparison of label noise and dataset bias. We then summarize and compare the representative approaches on (1) emotion feature extraction, including both handcrafted and deep features, (2) learning methods on dominant emotion recognition, personalized emotion prediction, emotion distribution learning, and learning from noisy data or few labels, and (3) AICA based applications. Finally, we discuss some challenges and promising research directions in the future, such as image content and context understanding, group emotion clustering, and viewer-image interaction.

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Identifying Emotions Aroused from Paintings

Cited by 10 publications

References 20 publications

Detection of Emotions in Artworks Using a Convolutional Neural Network Trained on Non-Artistic Images: A Methodology to Reduce the Cross-Depiction Problem

Detection of Emotions in Artworks Using a Convolutional Neural Network Trained on Non-Artistic Images: A Methodology to Reduce the Cross-Depiction Problem

Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion

Affective Image Content Analysis: Two Decades Review and New Perspectives

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