Researchers at Disney Research Lab, Pittsburgh, have developed a new automated method that assembles story-driven photo albums with ease.
Though taking photos is easy, editing a mass of vacation photos into an album is a Herculean task.
The new method, developed by a team led by Leonid Sigal, senior research scientist at Disney Research, attempts to not only select photos based on quality and relevance but also to order them in a way that makes narrative sense.
"Professional photographers, whether they are assembling a wedding album or a photo slideshow know that the strict chronological order of the photos is often less important than the story that is being told," said Sigal.
"But this process can be labourious particularly when large photo collections are involved. So we looked for ways to automate it," he added.
The team looked at the ways of arranging vacation photos into a coherent album.
They built a model that could create albums based on variety of photo features, including the presence or absence of faces and their spatial layout, overall scene textures and colours and the aesthetic quality of each image.
Their model also incorporated learned rules for how albums are assembled, such as preferences for certain types of photos to be placed at the beginning, in the middle and at the end of albums.
Exclusionary rules, such as avoiding the use of the same type of photo more than once, were also learned and incorporated.
The researchers used a machine learning algorithm to enable the system to learn how humans use those features and what rules they use to assemble photo albums.
The training sets used for this purpose were created for the study from thousands of photos from photo-sharing website Flickr.
Once the system learned the principles of selecting and ordering photos, it was able to compose photo albums from unordered and untagged collections of photos.
Sigal noted that such a system also can learn the preferences of individuals in assembling these collections to customise the album creation process.