Nonparametric Bayesian Texture Learning and Synthesis
We present a nonparametric Bayesian method for texture learning and synthesis. A texture image is represented by a 2D-Hidden Markov Model (2D-HMM) where the hidden states correspond to the cluster labeling of textons and the transition matrix encodes their spatial layout (the compatibility between adjacent textons). 2D-HMM is coupled with the Hierarchical Dirichlet process (HDP) which allows the number of textons and the complexity of transition matrix grow as the input texture becomes irregular. The HDP makes use of Dirichlet process prior which favors regular textures by penalizing the model complexity. This framework (HDP-2D-HMM) learns the texton vocabulary and their spatial layout jointly and automatically. The HDP-2D-HMM results in a compact representation of textures which allows fast texture synthesis with comparable rendering quality over the state-of-the-art image-based rendering methods. We also show that HDP-2D-HMM can be applied to perform image segmentation and synthesis.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationSemantic SegmentationTexture SynthesisSimilar Papers 제목 키워드 기반
Predictive Density Combination Using a Tree-Based Synthesis Function
Bayesian predictive synthesis (BPS) provides a method for combining multiple predictive distributions based on agent/expert opinion analysis theory and encompasses a range of existing density forecast pooling methods. Th…
regressionA generative nonparametric Bayesian model for whole genomes
Generative probabilistic modeling of biological sequences has widespread existing and potential use across biology and biomedicine, particularly given advances in high-throughput sequencing, synthesis and editing. Howeve…
Density Estimationmodelparameter estimationA generative nonparametric Bayesian model for whole genomes
Generative probabilistic modeling of biological sequences has widespread existing and potential use across biology and biomedicine, particularly given advances in high-throughput sequencing, synthesis and editing. Howeve…
Density Estimationparameter estimationBayesian Nonparametrics: An Alternative to Deep Learning
Bayesian nonparametric models offer a flexible and powerful framework for statistical model selection, enabling the adaptation of model complexity to the intricacies of diverse datasets. This survey intends to delve into…
Deep LearningElectrical EngineeringModel SelectionMulti-Object Tracking+2A Bayesian Nonparametric Perspective on Mahalanobis Distance for Out of Distribution Detection
Bayesian nonparametric methods are naturally suited to the problem of out-of-distribution (OOD) detection. However, these techniques have largely been eschewed in favor of simpler methods based on distances between pre-t…
Out-of-Distribution DetectionOut of Distribution (OOD) Detection