AutoCollage is an automatic procedure for constructing a visually appealing collage from a collection of input images. The aim is that the resulting collage should be representative of the collection, summarising its main themes. It is also assembled largely seamlessly, using graph-cut, Poisson blending of alpha-masks, to hide the joins between input images. This work makes several new contributions. Firstly, we show how energy terms can be included
that: encourage the selection of a representative set of images; that are sensitive to particular object classes; that encourage a spatially efficient and seamless layout. Secondly the resulting optimization poses a search problem that, on the face of it, is computationally infeasible. Rather than attempt an expensive, integrated optimization procedure, we have developed a sequence of optimization steps, from static ranking of images, through region of interest optimization, optimal packing by constraint satisfaction, and lastly graph-cut alpha-expansion. To illustrate the power of AutoCollage, we have used it to create collages of many home photo sets; we also conducted a user study in which AutoCollage outperformed competitive methods.

1. C. Rother, L. Bordeaux, Y. Hamadi, A. Blake, AutoCollage, ACM Transactions on Graphics (SIGGRAPH), August 2006
2. C. Rother, S. Kumar, V. Kolmogorov, A. Blake. Digital Tapestry, 2005 San Diego, CA, US Proc. IEEE Computer Vision and Pattern Recognition (CVPR).