Computational Understanding of Visual Interestingness Beyond Semantics

Abstract

Understanding visual interestingness is a challenging task addressed by researchers in various disciplines ranging from humanities and psychology to, more recently, computer vision and multimedia. The rise of infographics and the visual information overload that we are facing today have given this task a crucial importance. Automatic systems are increasingly needed to help users navigate through the growing amount of visual information available, either on the web or our personal devices, for instance by selecting relevant and interesting content. Previous studies indicate that visual interest is highly related to concepts like arousal, unusualness, or complexity, where these connections are found based on psychological theories, user studies, or computational approaches. However, the link between visual interestingness and other related concepts has been only partially explored so far, for example, by considering only a limited subset of covariates at a time. In this article, we present a comprehensive survey on visual interestingness and related concepts, aiming to bring together works based on different approaches, highlighting controversies, and identifying links that have not been fully investigated yet. Finally, we present some open questions that may be addressed in future works. Our work aims to support researchers interested in visual interestingness and related subjective or abstract concepts, providing an in-depth overlook at state-of-the-art theories in humanities and methods in computational approaches, as well as providing an extended list of datasets.