Cosaliency: Where People Look When Comparing Images
An overview of our algorithm and its results for two pairs of images. From left to right: Standard thumbnails for the input image pair, our calculated model for image cosaliency, its processed version and our automatically generated collection-aware crops. Note that small image features like the position of the woman's arm or the angle of the bird's head are nearly impossible to see using standard thumbnails alone.
Image triage is a common task in digital photography. Determining
which photos are worth processing for sharing with
friends and family and which should be deleted to make room
for new ones can be a challenge, especially on a device with a
small screen like a mobile phone or camera. In this work we
explore the importance of local structure changes--e.g. human
pose, appearance changes, object orientation, etc.--to the
photographic triage task. We perform a user study in which
subjects are asked to mark regions of image pairs most useful
in making triage decisions. From this data, we train a
model for image saliency in the context of other images that
we call cosaliency. This allows us to create collection-aware
crops that can augment the information provided by existing
thumbnailing techniques for the image triage task.
Citation: David E. Jacobs, Dan B Goldman, and Eli Shechtman. Cosaliency: Where People Look When Comparing Images. In UIST 2010, Proc. ACM Symposium on User Interface Software and Technology, October 2010.