  The field of Computer Vision (CV) is increasingly shifting towards
``high-level'' visual sensemaking tasks, yet the exact nature of these tasks
remains unclear and tacit. This survey paper addresses this ambiguity by
systematically reviewing research on high-level visual understanding, focusing
particularly on Abstract Concepts (ACs) in automatic image classification. Our
survey contributes in three main ways: Firstly, it clarifies the tacit
understanding of high-level semantics in CV through a multidisciplinary
analysis, and categorization into distinct clusters, including commonsense,
emotional, aesthetic, and inductive interpretative semantics. Secondly, it
identifies and categorizes computer vision tasks associated with high-level
visual sensemaking, offering insights into the diverse research areas within
this domain. Lastly, it examines how abstract concepts such as values and
ideologies are handled in CV, revealing challenges and opportunities in
AC-based image classification. Notably, our survey of AC image classification
tasks highlights persistent challenges, such as the limited efficacy of massive
datasets and the importance of integrating supplementary information and
mid-level features. We emphasize the growing relevance of hybrid AI systems in
addressing the multifaceted nature of AC image classification tasks. Overall,
this survey enhances our understanding of high-level visual reasoning in CV and
lays the groundwork for future research endeavors.
