Advances and Challenges in Computational Image Aesthetics

Abstract

Computational image aesthetics aims at designing algorithmic approaches to perform aesthetic decisions, in a similar fashion as humans. In the past fifteen years, computational aesthetics has undergone unprecedented development, thanks to the availability of large annotated datasets and deep learning approaches, impacting many applications in multimedia from image enhancement to recommendation and retrieval. In this chapter, we first overview the several interpretations that aesthetics has received over the centuries and propose a set of suitable dimensions for a taxonomy of computational aesthetic approaches. Then, we present the advances of computational aesthetics in the past decade by providing a critical analysis of the most popular datasets, early methods based on hand-crafted features, and modern approaches using deep neural networks. In the last part of the chapter, we discuss some open challenges in computational aesthetic quality assessment: dealing with the intrinsic subjectivity of the scores, and providing explainable aesthetic predictions. In particular, throughout the chapter, we stress the fundamental importance of data collection in computational aesthetics.