Adaptive ranking of perceptual aesthetics

• Present the concept of adaptive ranking list, which captures not only the relative order of ranked subjects but also a user's sensitivity level to the subjects.
• Extract discriminative features from weakly labelled images that helps further learning with only limited labelled training data.
• Compare similarity measurements of ranking lists with ties.

As humans, we love to rank things. Top ten lists exist for everything from movie stars to scary animals. Ambiguities (i.e. ties) naturally occur in the process of ranking when people feel they cannot distinguish two items. Human reported rankings derived from star ratings abound on recommendation websites such as Yelp and Netflix. However, those websites differ in star precision which points to the need for ranking systems that adapt to an individual user׳s preference sensitivity. In this work we propose an adaptive system that allows for ties when collecting ranking data. Using this system, we propose a framework for obtaining computer-generated rankings. We test our system and a computer-generated ranking method on the problem of evaluating facial aesthetics. Since aesthetics is a personalized and subjective issue, and it is hard to obtain large amount of aesthetics rankings from each user, we extract low-dimensional discriminative features from weakly labelled facial images and apply them to afterward learning. Extensive experimental evaluations and analysis well demonstrate the effectiveness of our work.