Journal
IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS
Volume 17, Issue 1, Pages 135-146Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TII.2020.2982995
Keywords
Image segmentation; Task analysis; Diffusion processes; Training; Manifolds; Informatics; Estimation; Affinity propagation; image segmentation; label pair diffusion (LPD); manifold learning; semisupervised learning
Categories
Funding
- National Science Foundation of China [U1713208, 61673220, 61972213]
- Natural Science Foundation of Jiangsu Province, China [BK20180458, BK20180069]
Ask authors/readers for more resources
This article explores the relationships between image element pairs and label pairs, extends label diffusion to label pair diffusion, and designs a probability learning process on a tensor product graph to smooth label priors. The proposed algorithm maintains computational efficiency and achieves superior performance in segmentation tasks.
This article explores the relationships between image element pairs and label pairs and extends label diffusion to label pair diffusion for the interactive image segmentation task. Compared with label diffusion, more accurate relationships between unlabeled and labeled data can be captured on a tensor product graph (TPG) by using higher order information, and more complex interactions among image elements and finer relationships between image element pairs and label pairs are explored in label pair diffusion (LPD) process. We first establish a prior label estimation framework to measure the label pair prior probability. Then, a probability learning process on TPG is designed to smooth the label prior. The learning process is equivalent to an iterative LPD process on the original graph, which makes the proposed algorithm maintain computational efficiency. Finally, the unary label probabilities can be obtained by a total-probability-theorem-based conversion from the binary relationships. Experiments on popular segmentation data sets demonstrate the superior performance of the proposed method.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available