An algorithmic approach to estimate cognitive aesthetics of images relative to ground truth of human psychology through a large user study

Abstract

This research introduces a learning model that estimates the cognitive perception of aesthetics. Taking psychology into account, this bridges the gap between human and machine. The goal is to build a machine-learning model that can estimate beauty in images perceived by human eyes. We have summand our research [Firoze, A., Osman, T., Psyche, S. S., & Rahman, R. M. (2018). Scoring photographic rule of thirds in a large MIRFLICKR dataset: A showdown between machine perception and human perception of image aesthetics. Asian Conference on Intelligent Information and Database Systems (pp. 466–475), Springer; Osman, T., Psyche, S. S., Deb, T., Firoze, A., & Rahman, R. M. (2018). Differential color harmony: A robust approach for extracting Harmonic Color features and perceive aesthetics in a large dataset. International Conference on Big Data and Cloud Computing, Springer] together with the idea of humans’ personal preferences and achieved higher than state of the art performances. An extensive user study (374 participants) has been conducted to support claims. Several photographical compositional metrics have been used. Colour gradient, rule of thirds and human subject’s psychology has been picked as features. The consideration of user’s perspective or psychology is one of the key contributions of this research.