These findings provide an intuitive understanding of the low-level image features that motivate the affective response, but the small scale of studies from which the inferences have been drawn makes the results less convincing. Parsimony refers to the minimalistic structures that are used in a given representation, whereas orderliness refers to the simplest way of organizing these structures. Though the perception of simplicity is partially subjective to individual experiences, it can also be highly affected by two objective factors, parsimony and orderliness. Any stimulus pattern is always perceived in the most simplistic structural setting. confirm the hypothesis that curved contours lead to positive feelings and that sharp transitions in contours trigger a negative bias.Ĭomplexity of shapes - As enumerated in various works of art, humans visually prefer simplicity. Roundness - Studies indicate that geometric properties of visual displays convey emotions like anger and happiness. These studies indicate that roundness and complexity of shapes are fundamental to understanding emotions. In contrast to prior studies on image aesthetics, which intended to estimate the level of visual appeal, we try to leverage some of the psychological studies on characteristics of shapes and their effect on human emotions. In this work, we try to extend our understanding of some of the low-level features which have not been explored in the study of visual affect through extensive statistical analyses. There have been many psychological theories suggesting a link between human affective responses and the low-level features in images apart from the semantic content. Bridging this gap is considered the “holy grail” of computer vision and the multimedia community. However, there is a wide gap between what humans can perceive and feel and what can be explained using current computational image features. A computational perspective to this problem has interested many researchers and resulted in articles on modeling the emotional and aesthetic content in images. The study of human visual preferences and the emotions imparted by various works of art and natural images has long been an active topic of research in the field of visual arts and psychology. Finally, we distinguish images with strong emotional content from emotionally neutral images with high accuracy. We model emotions from a dimensional perspective in order to predict valence and arousal ratings which have advantages over modeling the traditional discrete emotional categories. We combine our shape features with other state-of-the-art features to show a gain in prediction and classification accuracy. Through experimental results on the International Affective Picture System (IAPS) dataset we provide evidence for the significance of roundness-angularity and simplicity-complexity on predicting emotional content in images. Our contributions include an in-depth statistical analysis to understand the relationship between shapes and emotions. However, no prior research has modeled the dimensionality of emotions aroused by roundness and angularity. Shapes and their characteristics such as roundness, angularity, simplicity, and complexity have been postulated to affect the emotional responses of human beings in the field of visual arts and psychology. We investigated how shape features in natural images influence emotions aroused in human beings.
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