.png){kind=icon}
返回
Probabilistic Color-by-Numbers: Suggesting Pattern Colorizations Using Factor Graphs
.png){kind=icon}
PRE
AI
DOI：10.1145/2461912.2461988.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
We present a probabilistic factor graph model for automatically coloring 2D patterns. The model is trained on example patterns to statistically capture their stylistic properties. It incorporates terms for enforcing both color compatibility and spatial arrangements of colors that are consistent with the training examples. Using Markov Chain Monte Carlo, the model can be sampled to generate a diverse set of new colorings for a target pattern. This general probabilistic framework allows users to guide the generated suggestions via conditional inference or additional soft constraints. We demonstrate results on a variety of coloring tasks, and we evaluate the model through a perceptual study in which participants judged sampled colorings to be significantly preferable to other automatic baselines.
Keyword:
Probabilistic modeling
factor graphs
colorization
graphic design
data-driven methods
Markov Chain Monte Carlo
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.5
论文数:
4.7K
被引数:
3.6W
机构
引用论文
分享收藏Synthesizing Open Worlds with Constraints using Locally Annealed Reversible Jump MCMC使用局部退火的可逆跳跃MCMC合成具有约束的开放世界
OAAI
OAAI分享收藏Scattering of MCF7 Cells by Heregulin ß-1 Depends on the MEK and p38 MAP Kinase Pathway.png)
PLoS ONEOAAI
PLoS ONEIF0
OAAI分享收藏The physiological effects of cigarette smoking: Implications for psychophysiological research吸烟的生理效应: 对心理生理学研究的启示
PREAI
分享收藏
查看更多内容