Image cropping based on order learning

A novel approach to image cropping, called crop region comparator (CRC), is proposed in this paper, which learns ordering relationships between aesthetic qualities of different crop regions. CRC employs the single-region refinement (SR) module and the inter-region correlation (IC) module. First, we design the SR module to identify essential information in an original image and consider the composition of each crop candidate. Thus, the SR module helps CRC to adaptively find the best crop region according to the essential information. Second, we develop the IC module, which aggregates the information across two crop candidates to analyze their differences effectively and estimate their ordering relationship reliably. Then, we decide the crop region based on the relative aesthetic scores of all crop candidates, computed by comparing them in a pairwise manner. Extensive experimental results demonstrate that the proposed CRC algorithm outperforms existing image cropping techniques on various datasets.