Automatic image cropping with a single fully convolutional network
Xiang Deng, Chaoran Cui, Huihui Liu, Xiushan Nie, Xiaoming Xi, Yilong Yin · 2018
Automatic image cropping is of great importance for improving visual quality of images. Existing methods typically repurpose classifiers to perform cropping. Instead, we frame image cropping as a regression problem to bounding boxes and associated visual quality scores. An end-to-end framework with fully convolutional network is proposed to predict bounding boxes and associated scores from full images in one evaluation, which accepts input images of arbitrary sizes. In this way, we circumvent the requirement of fixed-sized inputs from prevalent convolutional networks, and thus avoid impairing the visual quality of images. Additionally, the whole cropping pipeline is a single network, so it can be optimized end-to-end directly on cropping performance, which is different from existing methods requiring the processes of proposal generation and subsequent region feature extraction. Experiments on a public dataset confirm that our framework has competitive accuracy and speed to traditional approaches.