Improving Visual Saliency Computing With Emotion Intensity

The Impact of Ageing on Episodic Memory Retrieval: How Valence Influences Neural Functional Connectivity

Age-related decline in episodic memory is often linked to structural and functional changes in the brain. Here, we investigated how these alterations might affect functional connectivity during memory retrieval following exposure to emotional stimuli. Using functional magnetic resonance imaging (fMRI), participants viewed images with varying emotional valences (positive, negative, and neutral) followed by unrelated non-arousing videos and were then asked to retrieve an episodic detail from the previously shown video. We conducted Multivariate Pattern Analysis (MVPA) to identify regions with divergent responses between age groups, which then served as seeds in Seed-Based Connectivity (SBC) analyses. The results revealed an age-related decline in behavioural performance following exposure to negative stimuli but preserved performance following positive stimuli. Young adults exhibited increased functional connectivity following negative valence. Conversely, old adults displayed increased connectivity more scarcely, and only following positive valence. These findings point to an adaptive response of the impact of emotions on task performance that depends on neural adaptations related to ageing. This suggests that age-related changes in functional connectivity might underlie how emotions influence memory, highlighting the need to tailor memory support strategies in older adulthood.

Affective Image Content Analysis: Two Decades Review and New Perspectives

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.

Visual Expression of Emotion in Dynamic 3D Painting System Based on Emotion Synthesis Model

Emotion is a unique ability possessed by human beings as advanced creatures. Emotions give people a unique physical and mental experience. Assigning emotions to computer systems is one of the latest topics in artificial intelligence research. The purpose is to allow machines to achieve natural coordination between humans and computers. This article focuses on the visual expression of emotion in the dynamic three-dimensional painting system, creating an intelligent painting system and realizing a good user experience. In this paper, the discrete method is used to qualitatively analyze emotions, and the continuous method is used to quantify basic emotions, and emotional modeling and emotional quantitative analysis are proposed to realize quantitative analysis of emotions. Combining these two methods, a comprehensive method is proposed, which uses a continuous method to quantify the basic emotions of each discrete dimension, and finally superimposes them into a comprehensive emotional synthesis model. Emotion modeling is the basis of emotion visualization. Borrowing the relationship between emotion synthesis model and visual emotion elements, this article puts forward the concept of qualitative and quantitative visual emotion elements, and expounds that the multidimensional superposition of visual emotion elements makes dynamic three-dimensional painting system emotions. The experimental results in this article show that the emotional visualization scheme of 100 samples is tested by quantitative statistical methods to demonstrate its effectiveness. Starting from 5 points of concern, the emotion visualization method discussed in this article can indeed convey or suggest a certain positive emotion (the average value of experience, transitivity, and infectiousness > 2.5, and the variance is close to 0), but we also found this recognition at the same time The degree is not high enough, and individual differences are large (mean value < 2.5, variance close to 1). This can indicate that different subjects have different feelings and evaluations of this emotional visualization. As long as the difference is within a reasonable range, this emotional visualization also has practical value, and has the ability to convey or suggest emotions.