  Despite recent progress, computational visual aesthetic is still challenging.
Image cropping, which refers to the removal of unwanted scene areas, is an
important step to improve the aesthetic quality of an image. However, it is
challenging to evaluate whether cropping leads to aesthetically pleasing
results because the assessment is typically subjective. In this paper, we
propose a novel cascaded cropping regression (CCR) method to perform image
cropping by learning the knowledge from professional photographers. The
proposed CCR method improves the convergence speed of the cascaded method,
which directly uses random-ferns regressors. In addition, a two-step learning
strategy is proposed and used in the CCR method to address the problem of
lacking labelled cropping data. Specifically, a deep convolutional neural
network (CNN) classifier is first trained on large-scale visual aesthetic
datasets. The deep CNN model is then designed to extract features from several
image cropping datasets, upon which the cropping bounding boxes are predicted
by the proposed CCR method. Experimental results on public image cropping
datasets demonstrate that the proposed method significantly outperforms several
state-of-the-art image cropping methods.
