Context aware image resizing

Another excellent piece of work presented at SIGGRAPH 2007 is Shai Avidan and Ariel Shamir's context aware image resizing paper. The two scientists describe in their paper how images can be effectively resized removing those pixels containing the least amount of information. They define an energy function computed for each image pixel and then find connected regions of low energy pixels that run from top to bottom or left to right; they call these connected regions seams. Seams are those regions that can be removed without much loss in the image's content. The paper abstract better describes how this parameter-free method works utilizing a new image operator called seam carving.

Effective resizing of images should not only use geometric constraints, but consider the image content as well. We present a simple image operator called seam carving that supports content-aware image resizing for both reduction and expansion. A seam is an optimal 8-connected path of pixels on a single image from top to bottom, or left to right, where optimality is defined by an image energy function. By repeatedly carving out or inserting seams in one direction we can change the aspect ratio of an image. By applying these operators in both directions we can retarget the image to a new size. The selection and order of seams protect the content of the image, as defined by the energy function. Seam carving can also be used for image content enhancement and object removal. We support various visual saliency measures for defining the energy of an image, and can also include user input to guide the process. By storing the order of seams in an image we create multi-size images, that are able to continuously change in real time to fit a given size.