Image Segmentation Algorithms Overview

TL;DR

This paper reviews various image segmentation algorithms, comparing their strengths and weaknesses, and predicts future development trends by integrating different techniques in the field.

Contribution

It provides a comprehensive analysis and comparison of existing image segmentation methods and forecasts future research directions combining these approaches.

Findings

Region-based segmentation has high accuracy but high computational cost.

Weakly-supervised CNN methods reduce annotation effort.

Integration of algorithms is predicted to enhance segmentation performance.

Abstract

The technology of image segmentation is widely used in medical image processing, face recognition pedestrian detection, etc. The current image segmentation techniques include region-based segmentation, edge detection segmentation, segmentation based on clustering, segmentation based on weakly-supervised learning in CNN, etc. This paper analyzes and summarizes these algorithms of image segmentation, and compares the advantages and disadvantages of different algorithms. Finally, we make a prediction of the development trend of image segmentation with the combination of these algorithms.