# Fast Hybrid Image Retargeting

arXiv: https://arxiv.org/abs/2203.13595 · HTML: https://mlsanity.com/paper/2203.13595/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 25 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Contextual Encoder-Decoder Network for Visual Saliency Prediction](https://mlsanity.com/paper/1902.06634/) (2019)
- [Weakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting](https://mlsanity.com/paper/1708.02731/) (2017)
- [Cycle-IR: Deep Cyclic Image Retargeting](https://mlsanity.com/paper/1905.03556/) (2019)
- [DeepIR: A Deep Semantics Driven Framework for Image Retargeting](https://mlsanity.com/paper/1811.07793/) (2018)
- [Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks](https://mlsanity.com/paper/1910.08071/) (2019)
- [Self-Play Reinforcement Learning for Fast Image Retargeting](https://mlsanity.com/paper/2010.00909/) (2020)
- [Image Resizing by Reconstruction from Deep Features](https://mlsanity.com/paper/1904.08475/) (2019)
- [Image retargeting via Beltrami representation](https://mlsanity.com/paper/1710.04034/) (2017)

## Built on by (most cited first, 2 of 2)

- [Prune and Repaint: Content-Aware Image Retargeting for any Ratio](https://mlsanity.com/paper/2410.22865/) (2024)
- [OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment](https://mlsanity.com/paper/2209.04804/) (2022)

## Abstract (arXiv)

Image retargeting changes the aspect ratio of images while aiming to preserve content and minimise noticeable distortion. Fast and high-quality methods are particularly relevant at present, due to the large variety of image and display aspect ratios. We propose a retargeting method that quantifies and limits warping distortions with the use of content-aware cropping. The pipeline of the proposed approach consists of the following steps. First, an importance map of a source image is generated using deep semantic segmentation and saliency detection models. Then, a preliminary warping mesh is computed using axis aligned deformations, enhanced with the use of a distortion measure to ensure low warping deformations. Finally, the retargeted image is produced using a content-aware cropping algorithm. In order to evaluate our method, we perform a user study based on the RetargetMe benchmark. Experimental analyses show that our method outperforms recent approaches, while running in a fraction of their execution time.