Object proposals-based significance map for image retargeting

Diverse aspect ratios of display devices require adaptation of the image content to be displayed on them. Image retargeting pertains to changing the size of an image to adapt to the aspect ratio and spatial resolution of the display device. This is achieved while preserving the salient or important information and thereby reducing visible artifacts in the retargeted image. Seam carving techniques remove or insert least energy seams, one pixel wide paths from top to bottom or left to right of an image, iteratively in order to achieve the target display size. These techniques highly depend on an energy measure of a pixel in an image. Here, we propose a novel technique of defining energy of a pixel, also known as significance map, using object proposal boxes with the probability of objects being present. Object proposal boxes are modeled using the Gaussian kernels. The generation of significance map can be executed on an average in just over a second for a given image. We show that the proposed significance map is able to capture salient region(s) of an image having different textured background and different noise levels. The significance map can be used with an image retargeting algorithm such as seam carving to achieve desired results. Quantitative assessment and comparison with the other state-of-the-art image retargeting techniques show that the proposed energy measure yields the best results for image retargeting using seam carving.