Interactive Segmentation With Prototype Learning for Few-Shot Root Annotation

Xiaolei Guo

doi:10.1109/TGRS.2025.3556799

Interactive Segmentation With Prototype Learning for Few-Shot Root Annotation

2025-01-01

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AI

DOI：10.1109/TGRS.2025.3556799

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Fine-scale pixel-level annotation of minirhizotron root images is a less common and challenging task. We present an interactive segmentation framework to accelerate root annotation. We leverage the concept of few-shot segmentation so that the pretrained model can be effectively fine-tuned and transferred to an unseen category. To provide immediate feedback for real-time interaction, we adapted a UNet architecture by attaching lightweight embedding layers which leveraged a prototype learning (PL) approach to efficiently learn the data metric in the embedding space. The prototypes optimized by the prototype loss preserve the within-class data variation, enabling effective fine-tuning. Furthermore, we designed a system with our interactive annotation framework and experimented with real users to validate the approach.

Keyword:

Image segmentation

Annotations

Training

Adaptation models

Prototypes

Feature extraction

Data models

Transfer learning

Computational modeling

Real-time systems

Deep metric learning (DML)

human-computer interaction

interactive annotation

interactive segmentation

minirhizotron root image

prototype learning (PL)

transfer learning

user study

Interactive Segmentation With Prototype Learning for Few-Shot Root Annotation

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