Automatic image cropping using sparse coding

Image cropping is a technique to help people improve their taken photos' quality by discarding unnecessary parts of a photo. In this paper, we propose a new approach to crop the photo for better composition through learning the structure. Firstly, we classify photos into different categories. Then we extract the graph-based visual saliency map of these photos, based on which we build a dictionary for each categories. Finally, by solving the sparse coding problem of each input photo based on the dictionary, we find a cropped region that can be best decoded by this dictionary. The experimental results demonstrate that our technique is applicable to a wide range of photos and produce more agreeable resulting photos.