  Real-world applications could benefit from the ability to automatically
retarget an image to different aspect ratios and resolutions, while preserving
its visually and semantically important content. However, not all images can be
equally well processed that way. In this work, we introduce the notion of image
retargetability to describe how well a particular image can be handled by
content-aware image retargeting. We propose to learn a deep convolutional
neural network to rank photo retargetability in which the relative ranking of
photo retargetability is directly modeled in the loss function. Our model
incorporates joint learning of meaningful photographic attributes and image
content information which can help regularize the complicated retargetability
rating problem. To train and analyze this model, we have collected a database
which contains retargetability scores and meaningful image attributes assigned
by six expert raters. Experiments demonstrate that our unified model can
generate retargetability rankings that are highly consistent with human labels.
To further validate our model, we show applications of image retargetability in
retargeting method selection, retargeting method assessment and photo collage
generation.
