Q-learning optimization in a multi-agents system for image segmentation

TL;DR

This paper presents a multi-agent Q-learning system designed to optimize operator selection and parameter tuning in image segmentation tasks within computer vision, aiming to automate and improve the process.

Contribution

It introduces a novel multi-agent framework based on the Vowel approach that employs Q-learning to optimize operator sequencing and parameter settings for image segmentation.

Findings

Effective operator sequencing achieved

Improved segmentation accuracy demonstrated

Validation through implementation confirms viability

Abstract

To know which operators to apply and in which order, as well as attributing good values to their parameters is a challenge for users of computer vision. This paper proposes a solution to this problem as a multi-agent system modeled according to the Vowel approach and using the Q-learning algorithm to optimize its choice. An implementation is given to test and validate this method.