研究图像分割中评估指标对共识型动力学模型的影响
Understanding the Impact of Evaluation Metrics in Kinetic Models for Consensus-based Segmentation
- 将图像像素视为粒子系统，通过共识机制演化
- 证明可从多种损失函数中选择参数实现分割
- 适合关注模型评估与优化的计算机视觉研究者
本文扩展了近期提出的基于共识的图像分割动力学模型。将二维图像的像素集合视为一个相互作用的粒子系统，其随时间演化依赖于像素间的交互和外部噪声驱动的共识过程。借助该模型的动力学形式，我们推导出其大时间解。结果表明，分割任务的参数选择可基于多种刻画评估指标的损失函数进行调整。
原文摘要 · Abstract (English)
In this article we extend a recently introduced kinetic model for consensus-based segmentation of images. In particular, we will interpret the set of pixels of a 2D image as an interacting particle system which evolves in time in view of a consensus-type process obtained by interactions between pixels and external noise. Thanks to a kinetic formulation of the introduced model we derive the large time solution of the model. We will show that the choice of parameters defining the segmentation task can be chosen from a plurality of loss functions characterising the evaluation metrics.
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