  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.
