A robust multi-agent Negotiation for advanced Image Segmentation: Design and Implementation
Keywords:Image segmentation, Region merging, Multi-Agent System, Game Theory, Coalition, Negotiation
It is generally accepted that segmentation is a critical problem that influences subsequent tasks during image processing. Often, the proposed approaches provide effectiveness for a limited type of images with a significant lack of a global solution. The difficulty of segmentation lies in the complexity of providing a global solution with acceptable accuracy within a reasonable time. To overcome this problem, some solutions combined several methods. This paper presents a method for segmenting 2D/3D images by merging regions and solving problems encountered during the process using a multi-agent system (MAS). We are using the strengths of MAS by opting for a compromise that satisfies segmentation by agentsâ€™ acts. Regions with high similarity are merged immediately, while the others with low similarity are ignored. The remaining ones, with ambiguous similarity, are solved in a coalition by negotiation. In our system, the agents make decisions according to the utility functions adopting the Pareto optimal in Game theory. Unlike hierarchical merging methods, MAS performs a hypothetical merger planning then negotiates the agreements' subsets to merge all regions at once.
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Copyright (c) 2019 Iberamia & The Authors
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Inteligencia Artificial (Ed. IBERAMIA)
ISSN: 1988-3064 (on line).
(C) IBERAMIA & The Authors