Computational Emotion Analysis From Images: Recent Advances and Future Directions
It provides a survey for researchers in computer vision and affective computing, but is incremental as it focuses on summarizing existing work rather than introducing new methods.
This paper reviews recent computational methods for analyzing emotions from images, summarizing key approaches, datasets, and results in the field.
Emotions are usually evoked in humans by images. Recently, extensive research efforts have been dedicated to understanding the emotions of images. In this chapter, we aim to introduce image emotion analysis (IEA) from a computational perspective with the focus on summarizing recent advances and suggesting future directions. We begin with commonly used emotion representation models from psychology. We then define the key computational problems that the researchers have been trying to solve and provide supervised frameworks that are generally used for different IEA tasks. After the introduction of major challenges in IEA, we present some representative methods on emotion feature extraction, supervised classifier learning, and domain adaptation. Furthermore, we introduce available datasets for evaluation and summarize some main results. Finally, we discuss some open questions and future directions that researchers can pursue.