Rating Distribution and Personality Prediction for ImageAesthetics Assessment

Abstract

Aesthetics has been an area of intensive interests and continuing exploration for long in multiple disciplines, such as philology, psychology, arts, photography, computer graphics, media, industrial design, and so on. Objective image aesthetic assessment (IAA) is related to three major considerations. First of all, technical quality assessment (TQA) of images still plays an important role in general, because basic visual features (e.g., contrast, brightness, colorfulness and semantic information) definitely influence humans' perception and experience. TQA has been already relatively better developed during the past two decades, so to be successful, IAA needs to focus more on the other two considerations that are special to it. The first consideration special to IAA is generic IAA (GIAA) which deals with aesthetic factors common to a typical human being or user, with examples of Rule of Thirds, symmetry, depth of field, object saliency, color Harmony, etc. The last special consideration is personalized IAA (PIAA) that is crucial in enabling many practical tasks, because "beauty is in the eye of the beholder". Compared with mere TQA, IAA is expected to be much more individualized, and machine learning provides an effective mean for the related tasks to be tackled. This talk will therefore introduce and discuss two issues particularly significant to PIAA: rating distribution prediction and personality-assisted aesthetic assessment. For the former, objective prediction will be demonstrated to be able to predict the subjective rating distribution (rather just the mean opinion score (MOS) in most existing TQA methods), since PIAA may have higher diversity of opinions from subjects (even with twin-peak distribution), especially for abstract art images. In such situations, a simple MOS estimation alone is far from the real opinions of aesthetics. For the latter, viewers/users of different personality (determined by the oft-used Big-Five scheme) have different preferences toward various categories of images. Personality prediction and its use in PIAA will be explored in hopes of creating more awareness and trigger further work in the related field.