OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment

• URL: http://arxiv.org/abs/2209.04804v1
• Date: Sun, 11 Sep 2022 07:16:59 GMT
• Title: OAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment
• Authors: Mohammad Reza Naderi, Mohammad Hossein Givkashi, Nader Karimi, Shahram Shirani, Shadrokh Samavi
• Abstract summary: Previous image methods create outputs that suffer from artifacts and distortions. Simultaneous resizing of the foreground and background causes changes in the aspect ratios of the objects. We propose a method that overcomes these problems.
• Score: 11.031841470875571
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Image retargeting aims at altering an image size while preserving important content and minimizing noticeable distortions. However, previous image retargeting methods create outputs that suffer from artifacts and distortions. Besides, most previous works attempt to retarget the background and foreground of the input image simultaneously. Simultaneous resizing of the foreground and background causes changes in the aspect ratios of the objects. The change in the aspect ratio is specifically not desirable for human objects. We propose a retargeting method that overcomes these problems. The proposed approach consists of the following steps. Firstly, an inpainting method uses the input image and the binary mask of foreground objects to produce a background image without any foreground objects. Secondly, the seam carving method resizes the background image to the target size. Then, a super-resolution method increases the input image quality, and we then extract the foreground objects. Finally, the retargeted background and the extracted super-resolued objects are fed into a particle swarm optimization algorithm (PSO). The PSO algorithm uses aesthetic quality assessment as its objective function to identify the best location and size for the objects to be placed in the background. We used image quality assessment and aesthetic quality assessment measures to show our superior results compared to popular image retargeting techniques.

Related papers

• Prune and Repaint: Content-Aware Image Retargeting for any Ratio [8.665919238538143]
  We propose a content-aware method called PruneRepaint to balance the preservation of key semantics and image quality. By focusing on the content and structure of the foreground, our PruneRepaint approach adaptively avoids key content loss and deformation.
  arXiv  Detail & Related papers  (2024-10-30T10:02:42Z)
• DiffUHaul: A Training-Free Method for Object Dragging in Images [78.93531472479202]
  We propose a training-free method, dubbed DiffUHaul, for the object dragging task. We first apply attention masking in each denoising step to make the generation more disentangled across different objects. In the early denoising steps, we interpolate the attention features between source and target images to smoothly fuse new layouts with the original appearance.
  arXiv  Detail & Related papers  (2024-06-03T17:59:53Z)
• Diff-DOPE: Differentiable Deep Object Pose Estimation [29.703385848843414]
  We introduce Diff-DOPE, a 6-DoF pose refiner that takes as input an image, a 3D textured model of an object, and an initial pose of the object. The method uses differentiable rendering to update the object pose to minimize the visual error between the image and the projection of the model. We show that this simple, yet effective, idea is able to achieve state-of-the-art results on pose estimation datasets.
  arXiv  Detail & Related papers  (2023-09-30T18:52:57Z)
• Supervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions [11.031841470875571]
  Image aims to alter the size of the image with attention to the contents. Labeled datasets are unavailable for training deep learning models in the image tasks. Regular convolutional neural networks cannot generate images of different sizes in inference time.
  arXiv  Detail & Related papers  (2023-06-12T19:17:44Z)
• Background Prompting for Improved Object Depth [70.25467510077706]
  Estimating the depth of objects from a single image is a valuable task for many vision, robotics, and graphics applications. We propose a simple yet effective Background Prompting strategy that adapts the input object image with a learned background. Results on multiple synthetic and real datasets demonstrate consistent improvements in real object depths for a variety of existing depth networks.
  arXiv  Detail & Related papers  (2023-06-08T17:59:59Z)
• Self-Supervised Video Object Segmentation via Cutout Prediction and Tagging [117.73967303377381]
  We propose a novel self-supervised Video Object (VOS) approach that strives to achieve better object-background discriminability. Our approach is based on a discriminative learning loss formulation that takes into account both object and background information. Our proposed approach, CT-VOS, achieves state-of-the-art results on two challenging benchmarks: DAVIS-2017 and Youtube-VOS.
  arXiv  Detail & Related papers  (2022-04-22T17:53:27Z)
• Fast Hybrid Image Retargeting [0.0]
  We propose a method that quantifies and limits warping distortions with the use of content-aware cropping. Our method outperforms recent approaches, while running in a fraction of their execution time.
  arXiv  Detail & Related papers  (2022-03-25T11:46:06Z)
• You Better Look Twice: a new perspective for designing accurate detectors with reduced computations [56.34005280792013]
  BLT-net is a new low-computation two-stage object detection architecture. It reduces computations by separating objects from background using a very lite first-stage. Resulting image proposals are then processed in the second-stage by a highly accurate model.
  arXiv  Detail & Related papers  (2021-07-21T12:39:51Z)
• Scale Normalized Image Pyramids with AutoFocus for Object Detection [75.71320993452372]
  A scale normalized image pyramid (SNIP) is generated that, like human vision, only attends to objects within a fixed size range at different scales. We propose an efficient spatial sub-sampling scheme which only operates on fixed-size sub-regions likely to contain objects. The resulting algorithm is referred to as AutoFocus and results in a 2.5-5 times speed-up during inference when used with SNIP.
  arXiv  Detail & Related papers  (2021-02-10T18:57:53Z)
• High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling [122.06593036862611]
  Existing image inpainting methods often produce artifacts when dealing with large holes in real applications. We propose an iterative inpainting method with a feedback mechanism. Experiments show that our method significantly outperforms existing methods in both quantitative and qualitative evaluations.
  arXiv  Detail & Related papers  (2020-05-24T13:23:45Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.