Evaluation and Prediction of Evoked Emotions Induced by Image Manipulations

Various image editing tools make our pictures more attractive, and at the same time, evoke different emotional responses. With powerful and easy-to-use imaging applications, capturing, editing and then sharing pictures have become daily life for many. This paper investigates the influence of several image manipulations on evoked emotions for different types of images. To do so, various types of images clustered in different categories, were collected from Instagram and subjective evaluations were conducted via crowdsourcing to gather the emotional responses on different manipulations as perceived by subjects. Evaluation results show that certain image manipulations can induce different evoked emotions on transformed pictures when compared to the original ones. However, such changes in image emotions due to manipulation are highly content dependent. Then, we conducted a machine learning based experiment, in attempt to predict the emotions of a manipulated image given its original version and the desired manipulation method. Experimental results present a promising performance of such a prediction model, which could pave the road to automatic selection or recommendation of image editing tools that can efficiently transform or emphasize desired emotions in pictures.