Interactive Image Segmentation Based on Label Pair Diffusion

doi:10.1109/TII.2020.2982995

Interactive Image Segmentation Based on Label Pair Diffusion

2021-01-01

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DOI：10.1109/TII.2020.2982995

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Abstract

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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.

Keywords:

Image segmentation

Task analysis

Diffusion processes

Training

Manifolds

Informatics

Estimation

Affinity propagation

image segmentation

label pair diffusion (LPD)

manifold learning

semisupervised learning

Interactive Image Segmentation Based on Label Pair Diffusion

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