BACKGROUND

Traditionally, people who used cameras for film photography were at least somewhat concerned about composing photos taken with such cameras to be visually pleasing. Once taken, there was little, if anything, people who did not develop their own film could do to improve their photos. Consequently, a photo that was not visually pleasing when taken generally resulted in a developed photo that also was not visually pleasing.

In digital photography, however, a photo that is not visually pleasing when taken may, through photo processing techniques, result in a visually pleasing (e.g., well-composed) photo. For example, a user with a digital camera may take a photo without being concerned generally about the composition of the photograph, such as whether the photo adheres to any of “the rules” of photography, e.g., rule-of-thirds, balancing elements, leading lines, and the like. Instead, the user may simply capture portions of a scene that they want to ensure are included in a final version of the photo. Given the digital photograph and a photo-editing application, a user may then perform a number of tasks to improve the photo, such as cropping the photo, adjusting the contrast of the photo, adjusting the brightness of the photo, adjusting the sharpness of the photo, applying a colored or black-and-white filter to the photo, and so on.

One of the most frequently performed tasks in photograph processing is cropping. Generally, the goal of cropping is to select a sub-region of a given image that is visually more pleasing than the image as a whole, e.g., a sub-region of the image that would be considered well-composed. However, users who are not familiar with “the rules” of photography, knowing when those rules may be broken, and/or who do not have a good “eye” for capturing a scene, may nevertheless crop an image so that the result is still not visually pleasing. Furthermore, manually cropping multiple photos may be time-consuming.

SUMMARY

Image cropping suggestion using multiple saliency maps is described. In one or more implementations, component scores, that are indicative of visual characteristics established for visually pleasing croppings, are computed for candidate image croppings using multiple different saliency maps. The visual characteristics on which the candidate image croppings are scored may be indicative of composition quality of a candidate cropping, whether content appearing in the scene is preserved by a candidate cropping, and simplicity of a boundary of a candidate cropping, for example. Based on the component scores, the croppings may be ranked with regard to each of the visual characteristics. The rankings may be used to cluster the candidate croppings into groups of similar croppings, such that the croppings in a group are different from one another by less than a threshold amount, but such that croppings in different groups are different from one another by at least the threshold amount.

Based on the clustering, croppings may then be chosen, e.g., to present the chosen croppings to a user for selection. To choose the croppings, they may be selected from different clusters. In this way, the croppings that are chosen for suggestion to a user may be different from one another by at least the threshold amount that is used to cluster the candidate croppings. Those croppings of a scene that are chosen may be suggested to a user through a user interface.

This Summary introduces a selection of concepts in a simplified form that are further described below in the Detailed Description. As such, this Summary is not intended to identify essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

