AutoCollage

Abstract

The paper defines 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 paper 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 in-feasible. 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.