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Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-Based Segmentation
Raffaella Fiamma Cabini1, Horacio Tettamanti2, Mattia Zanella2
1Euler Institute, Università della Svizzera Italiana, 6900 Lugano, Switzerland.
Entropy (Basel, Switzerland)
|February 26, 2025
Summary
This study introduces an advanced kinetic model for image segmentation, treating pixels as interacting particles. The model
Area of Science:
- Computational image analysis
- Mathematical modeling
- Image processing
Background:
- Existing kinetic models for image segmentation are extended.
- Image pixels are conceptualized as an interacting particle system.
Purpose of the Study:
- To derive the large time solution of the extended kinetic model.
- To explore the relationship between segmentation parameters and evaluation metrics.
Main Methods:
- Utilizing a kinetic formulation of the consensus-based model.
- Analyzing the evolution of the pixel system over time.
- Investigating interactions between pixels and external noise.
Main Results:
- The large time solution of the kinetic model is derived.
- Demonstration that segmentation parameters can be selected from various loss functions.
- Characterization of evaluation metrics for segmentation tasks.
Conclusions:
- The extended kinetic model provides a robust framework for image segmentation.
- The flexibility in choosing loss functions enhances the adaptability of the model.
- This work contributes to the theoretical understanding of consensus-based image segmentation.
