Towards a computerized estimation of visual complexity in images: Data to assess the association of computed visual complexity features to human responses in visual tasks

• Published in: Data in brief Vol. 32; p. 106108
• Main Authors: Aharonson, Vered, Babshet, Kanaka, Korczyn, Amos
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.10.2020; Elsevier
• Subjects: Black and white image stimuli; cognition; Cognitive tests; Computational attributes; computer literacy; Computer Science; computer vision; computers; data collection; gender; health care workers; humans; Image feature extraction; vision; Visual complexity; Visual recall; Visual recognition; Visual complexity; Black and white image stimuli; Image feature extraction; Visual recognition; Visual recall; Computational attributes; Cognitive tests
• DOI: 10.1016/j.dib.2020.106108
• ISSN: 2352-3409

Artificial vision has been extensively studied in the mathematical and computational Sciences. Concurrently, psychological studies attempt to describe visual cognition and the complexity of visual tasks as perceived by humans. The methods and the definitions of vision used by these two disciplines are disjointed. Particularly, an explanation of computer vision performance by human-perceived attributes, if attempted, can only be inferred. This article describes a dataset collected to explore the association between computer-extracted visual attributes and human-perceived attributes in the context of cognitive tasks. The data was acquired from a cohort of 406 subjects, ages 40–90, in the presence of a healthcare professional who assessed that the subjects had no cognitive or motor disorder. The subjects performed computerized cognitive tests which entailed tasks of recognition or recall of an image in a set of three images, presented on the computer screen. The images were simple black and white abstract square shapes. The latencies of the subjects’ responses, by keyboard key press, to each task were logged. The data contains 3 parts: the images presented in each task, described by binary vectors for black and white coding, a response time logged for each task and the subjects’ age, gender, and computer proficiency. A preliminary comparison of computationally-extracted complexity features and subjects’ performance is provided in the article entitled “Linking computerized and perceived attributes of visual complexity” [1].