Improved Otsu Multi-Threshold Image Segmentation Method based on Sailfish Optimization

Image segmentation is a key step from image processing to image analysis. The classical multi-threshold Otsu algorithm has achieved good results in image segmentation, but it is very time-consuming to use exhaustive search methods to find the optimal threshold. To solve this problem, this paper prop...

Full description

Saved in:
Bibliographic Details
Main Authors: Li, Ke, Bai, Ling, Li, Yinguo, Feng, Mingchi
Format: Conference Proceeding
Language:English
Subjects:
Distortion
Fitness function
Heuristic algorithms
Image analysis
Image segmentation
Otsu multi-threshold image segmentation
Process control
Search methods
Swordfish optimization
Time complexity
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Image segmentation is a key step from image processing to image analysis. The classical multi-threshold Otsu algorithm has achieved good results in image segmentation, but it is very time-consuming to use exhaustive search methods to find the optimal threshold. To solve this problem, this paper proposes an improved Otsu multi-threshold image segmentation method based on sailfish optimization(SFO). In order to reduce the time complexity of the algorithm, the heuristic search of sailfish biota is simulated to find the optimal threshold of image segmentation. The inter-class variance of multi-threshold is used as the fitness function of SFO, and the fitness value of each iteration is calculated. The final maximum fitness value is the optimal threshold of image segmentation. The experimental results show that the proposed algorithm in this paper not only improves the segmentation quality, but also shortens the optimization time, which demonstrates the correctness and efficiency of the algorithm.
ISSN:1948-9447
DOI:10.1109/CCDC52312.2021.9601664