Papers Image Retargeting
“Image Retargeting” 태그가 달린 논문 26편 · 필터 해제
HALO: Human-Aligned End-to-end Image Retargeting with Layered Transformations
Image retargeting aims to change the aspect-ratio of an image while maintaining its content and structure with less visual artifacts. Existing methods still generate many artifacts or fail to maintain original content or…
Image RetargetingPrune and Repaint: Content-Aware Image Retargeting for any Ratio
Image retargeting is the task of adjusting the aspect ratio of images to suit different display devices or presentation environments. However, existing retargeting methods often struggle to balance the preservation of ke…
Image RetargetingRetargeting video with an end-to-end framework
Video holds significance in computer graphics applications. Because of the heterogeneous of digital devices, retargeting videos becomes an essential function to enhance user viewing experience in such applications. In th…
Image RetargetingSupervised Deep Learning for Content-Aware Image Retargeting with Fourier Convolutions
Image retargeting aims to alter the size of the image with attention to the contents. One of the main obstacles to training deep learning models for image retargeting is the need for a vast labeled dataset. Labeled datas…
Deep LearningImage Quality AssessmentImage RetargetingOAIR: Object-Aware Image Retargeting Using PSO and Aesthetic Quality Assessment
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 distort…
Image Quality AssessmentImage RetargetingSuper-ResolutionFast Hybrid Image Retargeting
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 im…
Image RetargetingSaliency DetectionSemantic SegmentationSeeTheSeams: Localized Detection of Seam Carving based Image Forgery in Satellite Imagery
Seam carving is a popular technique for content aware image retargeting. It can be used to deliberately manipulate images, for example, change the GPS locations of a building or insert/remove roads in a satellite image. …
Image RetargetingInstance-Level Relative Saliency Ranking with Graph Reasoning
Conventional salient object detection models cannot differentiate the importance of different salient objects. Recently, two works have been proposed to detect saliency ranking by assigning different degrees of saliency …
Image Retargetingobject-detectionObject DetectionSaliency Ranking+1Self-Play Reinforcement Learning for Fast Image Retargeting
In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes. In one of the best-performing methods called MULTIOP, multiple retargeting operators were combined and retargeted…
Image Retargetingreinforcement-learningReinforcement LearningReinforcement Learning (RL)Deep Convolutional Neural Network for Identifying Seam-Carving Forgery
Seam carving is a representative content-aware image retargeting approach to adjust the size of an image while preserving its visually prominent content. To maintain visually important content, seam-carving algorithms fi…
Image ForensicsImage RetargetingImage Seam-Carving by Controlling Positional Distribution of Seams
Image retargeting is a new image processing task that renders the change of aspect ratio in images. One of the most famous image-retargeting algorithms is seam-carving. Although seam-carving is fast and straightforward, …
Image Quality AssessmentImage RetargetingDCIL: Deep Contextual Internal Learning for Image Restoration and Image Retargeting
Recently, there is a vast interest in developing methods which are independent of the training samples such as deep image prior, zero-shot learning, and internal learning. The methods above are based on the common goal o…
DiversityImage RestorationImage RetargetingSuper-Resolution+1Context-Aware Saliency Detection for Image Retargeting Using Convolutional Neural Networks
Image retargeting is the task of making images capable of being displayed on screens with different sizes. This work should be done so that high-level visual information and low-level features such as texture remain as i…
Image RetargetingSaliency DetectionSemantic SegmentationCycle-IR: Deep Cyclic Image Retargeting
Supervised deep learning techniques have achieved great success in various fields due to getting rid of the limitation of handcrafted representations. However, most previous image retargeting algorithms still employ fixe…
Image RetargetingDeepIR: A Deep Semantics Driven Framework for Image Retargeting
We present \emph{Deep Image Retargeting} (\emph{DeepIR}), a coarse-to-fine framework for content-aware image retargeting. Our framework first constructs the semantic structure of input image with a deep convolutional neu…
Image RetargetingPixel Objectness: Learning to Segment Generic Objects Automatically in Images and Videos
We propose an end-to-end learning framework for segmenting generic objects in both images and videos. Given a novel image or video, our approach produces a pixel-level mask for all "object-like" regions---even for object…
Foreground SegmentationImage RetargetingImage RetrievalImage Segmentation+7Image Retargetability
Real-world applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions, while preserving its visually and semantically important content. However, not all ima…
Image RetargetingImage retargeting via Beltrami representation
Image retargeting aims to resize an image to one with a prescribed aspect ratio. Simple scaling inevitably introduces unnatural geometric distortions on the important content of the image. In this paper, we propose a sim…
Image RetargetingNovel Evaluation Metrics for Seam Carving based Image Retargeting
Image retargeting effectively resizes images by preserving the recognizability of important image regions. Most of retargeting methods rely on good importance maps as a cue to retain or remove certain regions in the inpu…
Image RetargetingWeakly- and Self-Supervised Learning for Content-Aware Deep Image Retargeting
This paper proposes a weakly- and self-supervised deep convolutional neural network (WSSDCNN) for content-aware image retargeting. Our network takes a source image and a target aspect ratio, and then directly outputs a r…
Image RetargetingSelf-Supervised Learning