  Automatic image cropping algorithms aim to recompose images like human-being
photographers by generating the cropping boxes with improved composition
quality. Cropping box regression approaches learn the beauty of composition
from annotated cropping boxes. However, the bias of annotations leads to
quasi-trivial recomposing results, which has an obvious tendency to the average
location of training samples. The crux of this predicament is that the task is
naively treated as a box regression problem, where rare samples might be
dominated by normal samples, and the composition patterns of rare samples are
not well exploited. Observing that similar composition patterns tend to be
shared by the cropping boundaries annotated nearly, we argue to find the beauty
of composition from the rare samples by clustering the samples with similar
cropping boundary annotations, ie, similar composition patterns. We propose a
novel Contrastive Composition Clustering (C2C) to regularize the composition
features by contrasting dynamically established similar and dissimilar pairs.
In this way, common composition patterns of multiple images can be better
summarized, which especially benefits the rare samples and endows our model
with better generalizability to render nontrivial results. Extensive
experimental results show the superiority of our model compared with prior
arts. We also illustrate the philosophy of our design with an interesting
analytical visualization.
