A Comparative Study of Computational Aesthetics
This is an incremental study for computer vision researchers, showing limitations in existing computational aesthetics models.
The paper tackles the problem of quantifying image aesthetics beyond quality metrics, finding that generic descriptors match hand-crafted ones for global features, but both fail without spatial composition, and visual dictionaries perform poorly without spatial pyramids.
Objective metrics model image quality by quantifying image degradations or estimating perceived image quality. However, image quality metrics do not model what makes an image more appealing or beautiful. In order to quantify the aesthetics of an image, we need to take it one step further and model the perception of aesthetics. In this paper, we examine computational aesthetics models that use hand-crafted, generic and hybrid descriptors. We show that generic descriptors can perform as well as state of the art hand-crafted aesthetics models that use global features. However, neither generic nor hand-crafted features is sufficient to model aesthetics when we only use global features without considering spatial composition or distribution. We also follow a visual dictionary approach similar to state of the art methods and show that it performs poorly without the spatial pyramid step.