Sensation-based photo cropping

This paper proposes a novel method for automatically cropping a photo using a quality classifier that assesses whether the cropped region is agreeable to users. We statistically build this quality classifier using large photo collections available on websites where people manually insert quality scores to photos. We first trim the original image and then decide on the candidates for cropping. We find the cropped region with the highest quality score by applying the quality classifier to the candidates. Current automatic photo cropping techniques search for attention grabbing regions that consist of salient pixels from the original photo. They are not always pleasant to users because they do not take into account the quality of the cropped region. Our method with the quality classifier outperforms a state-of-the-art method that takes into consideration only the user's attention for automatic photo cropping.