Realistic image composite with best-buddy prior of natural image patches

Realistic image composite requires the appearance of foreground and background layers to be consistent. This is difficult to achieve because the foreground and the background may be taken from very different environments. This paper proposes a novel composite adjustment method that can harmonize appearance of different composite layers. We introduce the Best-Buddy Prior (BBP), which is a novel compact representations of the joint co-occurrence distribution of natural image patches. BBP can be learned from unlabelled images given only the unsupervised regional segmentation. The most-probable adjustment of foreground can be estimated efficiently in the BBP space as the shift vector to the local maximum of density function. Both qualitative and quantitative evaluations show that our method outperforms previous composite adjustment methods.