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Quantum Inspired Swarm Optimization for Multi-Level Image Segmentation Using BDSONN Architecture
Abstract
This chapter is intended to propose a quantum inspired self-supervised image segmentation method by quantum-inspired particle swarm optimization algorithm and quantum-inspired ant colony optimization algorithm, based on optimized MUSIG (OptiMUSIG) activation function with a bidirectional self-organizing neural network architecture to segment multi-level grayscale images. The proposed quantum-inspired swarm optimization-based methods are applied on three standard grayscale images. The performances of the proposed methods are demonstrated in comparison with their conventional counterparts. Experimental results are reported in terms of fitness value, computational time, and class boundaries for both methods. It has been noticed that the quantum-inspired meta-heuristic method is superior in terms of computational time in comparison to its conventional counterpart.
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