Saliency Driven Image Manipulation
This addresses the need for users to adjust image saliency for better visual focus, but it is incremental as it builds on existing saliency manipulation techniques.
The paper tackles the problem of manipulating images to control object saliency, such as enhancing important objects or attenuating distractors, by proposing an optimization framework that uses internal patches to achieve realistic results, showing significant improvement over previous methods.
Have you ever taken a picture only to find out that an unimportant background object ended up being overly salient? Or one of those team sports photos where your favorite player blends with the rest? Wouldn't it be nice if you could tweak these pictures just a little bit so that the distractor would be attenuated and your favorite player will stand-out among her peers? Manipulating images in order to control the saliency of objects is the goal of this paper. We propose an approach that considers the internal color and saliency properties of the image. It changes the saliency map via an optimization framework that relies on patch-based manipulation using only patches from within the same image to achieve realistic looking results. Applications include object enhancement, distractors attenuation and background decluttering. Comparing our method to previous ones shows significant improvement, both in the achieved saliency manipulation and in the realistic appearance of the resulting images.