Active Online Learning for Interactive Segmentation Using Sparse Gaussian Processes

Rudolph Triebel; Jan Stühmer; Mohamed Souiai; Daniel Cremers

doi:10.1007/978-3-319-11752-2_53

ScienceGate Book Chapters

BOOK-CHAPTER

Active Online Learning for Interactive Segmentation Using Sparse Gaussian Processes

Rudolph Triebel Jan Stühmer Mohamed Souiai Daniel Cremers

Year: 2014 Lecture notes in computer science Pages: 641-652 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-319-11752-2_53

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Artificial intelligence Inference Segmentation Image segmentation Benchmark (surveying) Gaussian process Pattern recognition (psychology) Online learning Classifier (UML) Gaussian Machine learning

Metrics

Cited By

1.08

FWCI (Field Weighted Citation Impact)

Refs

0.77

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Citation History

Topics

Gaussian Processes and Bayesian Inference

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning and Algorithms

Physical Sciences → Computer Science → Artificial Intelligence

Machine Learning and Data Classification

Physical Sciences → Computer Science → Artificial Intelligence

Active Online Learning for Interactive Segmentation Using Sparse Gaussian Processes

Metrics

Citation History

Topics

Related Documents

Online Informative Path Planning Using Sparse Gaussian Processes

Informative planning and online learning with sparse Gaussian processes

Learning Equity Volatility Surfaces Using Sparse Gaussian Processes

Online anomaly detection with sparse Gaussian processes

Haptic Preference Learning Using Active Learning and Gaussian Processes