# Saliency Driven Image Manipulation

arXiv: https://arxiv.org/abs/1612.02184 · HTML: https://mlsanity.com/paper/1612.02184/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Dec 2016.

## Built on by (most cited first, 9 of 9)

- [Boundary-Aware Segmentation Network for Mobile and Web Applications](https://mlsanity.com/paper/2101.04704/) (2021)
- [Deep Saliency Prior for Reducing Visual Distraction](https://mlsanity.com/paper/2109.01980/) (2021)
- [Realistic Saliency Guided Image Enhancement](https://mlsanity.com/paper/2306.06092/) (2023)
- [Unifying Global-Local Representations in Salient Object Detection with Transformer](https://mlsanity.com/paper/2108.02759/) (2021)
- [Neural Knitworks: Patched Neural Implicit Representation Networks](https://mlsanity.com/paper/2109.14406/) (2021)
- [A Saliency-Guided Street View Image Inpainting Framework for Efficient Last-Meters Wayfinding](https://mlsanity.com/paper/2205.06934/) (2022)
- [Salient Mask-Guided Vision Transformer for Fine-Grained Classification](https://mlsanity.com/paper/2305.07102/) (2023)
- [SPICE: A Synergistic, Precise, Iterative, and Customizable Image Editing Workflow](https://mlsanity.com/paper/2504.09697/) (2025)
- [SETGAN: Scale and Energy Trade-off GANs for Image Applications on Mobile Platforms](https://mlsanity.com/paper/2103.12896/) (2021)

## Abstract (arXiv)

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.