Image Quilting for Texture Synthesis and Transfer
E326786
"Image Quilting for Texture Synthesis and Transfer" is a seminal computer graphics paper that introduced a patch-based method for generating and transferring realistic textures in images.
All labels observed (2)
| Label | Occurrences |
|---|---|
| Image Quilting for Texture Synthesis and Transfer canonical | 2 |
| Example-Based Texture Synthesis | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T3094201 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Image Quilting for Texture Synthesis and Transfer Context triple: [Alexei Efros, notableWork, Image Quilting for Texture Synthesis and Transfer]
-
A.
Modeling image patches with a directed hierarchy of Markov random fields
"Modeling image patches with a directed hierarchy of Markov random fields" is a research paper that introduces a probabilistic hierarchical model for capturing complex statistical structure in image patches using directed Markov random fields.
-
B.
PixelRNN
PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
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C.
Deep Convolutional GAN
Deep Convolutional GAN is a widely used GAN architecture that replaces fully connected layers with deep convolutional layers to generate high-quality, realistic images.
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D.
Fréchet Inception Distance
Fréchet Inception Distance is a widely used quantitative metric that measures the similarity between real and generated images by comparing their feature distributions extracted from a pretrained Inception network.
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E.
Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Image Quilting for Texture Synthesis and Transfer Target entity description: "Image Quilting for Texture Synthesis and Transfer" is a seminal computer graphics paper that introduced a patch-based method for generating and transferring realistic textures in images.
-
A.
Modeling image patches with a directed hierarchy of Markov random fields
"Modeling image patches with a directed hierarchy of Markov random fields" is a research paper that introduces a probabilistic hierarchical model for capturing complex statistical structure in image patches using directed Markov random fields.
-
B.
PixelRNN
PixelRNN is a deep generative model that uses recurrent neural networks to sequentially model and generate images pixel by pixel.
-
C.
Deep Convolutional GAN
Deep Convolutional GAN is a widely used GAN architecture that replaces fully connected layers with deep convolutional layers to generate high-quality, realistic images.
-
D.
Fréchet Inception Distance
Fréchet Inception Distance is a widely used quantitative metric that measures the similarity between real and generated images by comparing their feature distributions extracted from a pretrained Inception network.
-
E.
Generative Adversarial Networks
Generative Adversarial Networks are a class of machine learning models in which two neural networks compete to generate highly realistic synthetic data, such as images, audio, or text.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
computer graphics paper
ⓘ
scientific paper ⓘ texture synthesis method ⓘ |
| algorithmicComponent |
dynamic programming for minimum error boundary cut
ⓘ
patch selection based on SSD error in overlap ⓘ |
| application |
graphics and image editing
ⓘ
stylization via texture transfer ⓘ texture generation for rendering ⓘ |
| approach |
minimum error boundary cut
ⓘ
overlapping image patches ⓘ patch-based texture synthesis ⓘ |
| assumption | textures can be modeled by local neighborhoods in example images ⓘ |
| author |
Alexei Efros
ⓘ
surface form:
Alexei A. Efros
William T. Freeman ⓘ |
| citationStatus | highly cited ⓘ |
| contribution |
demonstrated efficient texture synthesis from small exemplars
ⓘ
introduced image quilting algorithm ⓘ introduced patch-based texture transfer framework ⓘ |
| field |
computer graphics
ⓘ
image processing ⓘ texture synthesis ⓘ texture transfer ⓘ |
| goal |
realistic texture synthesis
ⓘ
texture transfer between images ⓘ |
| influenceOn |
graphcut-based image editing
ⓘ
non-parametric sampling approaches in vision ⓘ patch-based image inpainting methods ⓘ patch-based super-resolution methods ⓘ |
| input | sample texture image ⓘ |
| keyIdea |
assemble new images by stitching together small patches from an example texture
ⓘ
choose patches based on similarity in overlapping regions ⓘ compute optimal boundary cut to minimize visible seams ⓘ |
| language | English ⓘ |
| methodType |
example-based texture synthesis
ⓘ
non-parametric texture synthesis ⓘ |
| output |
image with transferred texture
ⓘ
synthetic texture image ⓘ |
| peerReviewed | true ⓘ |
| publicationYear | 2001 ⓘ |
| publishedBy | ACM ⓘ |
| publishedIn |
ACM SIGGRAPH
ⓘ
surface form:
SIGGRAPH 2001
|
| relatedConcept |
graph cuts
ⓘ
non-parametric sampling ⓘ patch-based image processing ⓘ |
| technique | image quilting ⓘ |
| title | Image Quilting for Texture Synthesis and Transfer self-link ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: Image Quilting for Texture Synthesis and Transfer Description of subject: "Image Quilting for Texture Synthesis and Transfer" is a seminal computer graphics paper that introduced a patch-based method for generating and transferring realistic textures in images.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
Example-Based Texture Synthesis
Image Quilting for Texture Synthesis and Transfer
→
title
→
Image Quilting for Texture Synthesis and Transfer
self-link
ⓘ