Human Perception of Visual Information [electronic resource] : Psychological and Computational Perspectives /

Preface -- Chapter 1 -- The Ingredients of Scenes That Affect Object Search and Perception -- Chapter 2 -- Exploring Deep Fusion Ensembling for Automatic Visual Interestingness Prediction -- Chapter 3 -- Affective Perception: The Power is in the Picture -- Chapter 4 -- Computational Emotion Analysis From Images: Recent Advances and Future Directions -- Chapter 5 -- The Interplay Of Objective And Subjective Factors In Empirical Aesthetics -- Chapter 6 -- Advances and Challenges in Computational Image Aesthetics -- Chapter 7 -- Shared Memories Driven by the Intrinsic Memorability of Items -- Chapter 8 -- Memorability: an Image-computable Measure of Information Utility -- Chapter 9 -- The Influence of Low -- and Mid-Level Visual Features on the Perception of Streetscape Qualities -- Chapter 10 -- Who Sees What? Examining Urban Impressions in Global South Cities.

Recent years have witnessed important advancements in our understanding of the psychological underpinnings of subjective properties of visual information, such as aesthetics, memorability, or induced emotions. Concurrently, computational models of objective visual properties such as semantic labelling and geometric relationships have made significant breakthroughs using the latest achievements in machine learning and large-scale data collection. There has also been limited but important work exploiting these breakthroughs to improve computational modelling of subjective visual properties. The time is ripe to explore how advances in both of these fields of study can be mutually enriching and lead to further progress. This book combines perspectives from psychology and machine learning to showcase a new, unified understanding of how images and videos influence high-level visual perception - particularly interestingness, affective values and emotions, aesthetic values, memorability, novelty, complexity, visual composition and stylistic attributes, and creativity. These human-based metrics are interesting for a very broad range of current applications, ranging from content retrieval and search, storytelling, to targeted advertising, education and learning, and content filtering. Work already exists in the literature that studies the psychological aspects of these notions or investigates potential correlations between two or more of these human concepts. Attempts at building computational models capable of predicting such notions can also be found, using state-of-the-art machine learning techniques. Nevertheless their performance proves that there is still room for improvement, as the tasks are by nature highly challenging and multifaceted, requiring thought on both the psychological implications of the human concepts, as well as their translation to machines.