Building an Image Database for Studying Image Retargeting

Modern electronic devices(such as TVs, laptops, and mobile devices) come with a huge variety in screen sizes, resolutions, and aspect ratios. Image retargeting is a technique to retarget or (resize) an image to better utilize the viewing device screen and to protect the main content of the image. Different retargeting techniques have been proposed in the literature that mainly utilizes one of the following main techniques: cropping, seam carving, and scale and stretch. The current problem of image retargeting is that it is very hard to determine the best technique to use on an image to get a target dimension. To apply techniques such as machine learning to determine the best technique to perform image retargeting, an annotated image set is needed to perform the training step. In this work, we build and annotate an image set that is suitable to develop such advance retargeting techniques. We build a dataset that include 500 original images. We apply 4 different retargeting techniques to get two different sizes. The resulting image set contains 4000 images annotated by three people. We also analyze the annotation results to get useful remarks from the annotators perceptual point of view.