Saliency Driven Image Manipulation

TL;DR

This paper introduces a saliency-driven image manipulation method that adjusts object prominence within images by optimizing internal image patches, enhancing object visibility and reducing distractors for more realistic results.

Contribution

It presents a novel patch-based optimization framework that manipulates saliency maps internally, improving saliency control and realism over previous methods.

Findings

Significant improvement in saliency manipulation effectiveness

Enhanced realism of manipulated images

Effective attenuation of distractors and background decluttering

Abstract

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.