Image completion is a process of removing an area from a photograph and replacing it with suitable data. Earlier methods either search for this relevant data within the image itself, or extends the search to some form of additional data, usually some form of database.

Methods that search for suitable data within the image itself has problems when no suitable data can be found in the image. Methods that extend their search has in earlier work either used some form of database with labeled images or a massive database with photos from the Internet. For the labels in a database to be useful they typically needs to be entered manually, which is a very time consuming process. Methods that uses databases with millions of images from the Internet has issues with copyrighted images, storage of the photographs and computation time.

This work shows that a small database of the user’s own private, or professional, photos can be used to improve the quality of image completions. A photographer today typically take many similar photographs on similar scenes during a photo session. Therefore a smaller number of images are needed to find images that are visually and structurally similar, than when random images downloaded from the internet are used.

Thus, this approach gains most of the advantages of using additional data for the image completions, while at the same time minimizing the disadvantages. It gains a better ability to find suitable data without having to process millions of irrelevant photos.