Color Image Segmentation Metrics

TL;DR

This paper reviews and compares various quantitative evaluation metrics for color image segmentation, highlighting their differences and implications for selecting appropriate measures in image analysis tasks.

Contribution

It provides an analytical and comparative review of segmentation metrics, aiding in understanding their impact on evaluation outcomes.

Findings

Different metrics favor different segmentation methods.

A conceptual comparison clarifies metric differences.

Selection of metrics influences evaluation results.

Abstract

An automatic image segmentation procedure is an inevitable part of many image analyses and computer vision which deeply affect the rest of the system; therefore, a set of interactive segmentation evaluation methods can substantially simplify the system development process. This entry presents the state of the art of quantitative evaluation metrics for color image segmentation methods by performing an analytical and comparative review of the measures. The decision-making process in selecting a suitable evaluation metric is still very serious because each metric tends to favor a different segmentation method for each benchmark dataset. Furthermore, a conceptual comparison of these metrics is provided at a high level of abstraction and is discussed for understanding the quantitative changes in different image segmentation results.