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Nature-Inspired Optimization Algorithms and Their Application in Multi-Thresholding Image Segmentation
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DOI：10.1007/s11831-019-09334-y.png)
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Abstract
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In the field of image processing, there are several problems where an efficient search of the solutions has to be performed within a complex search domain to find an optimal solution. Multi-thresholding which is a very important image segmentation technique is one of them. The multi-thresholding problem is simply an exponential combinatorial optimization process which traditionally is formulated based on complex objective function criterion which can be solved using only nondeterministic methods. Under such circumstances, there is also no unique measurement which quantitatively judges the quality of a given segmented image. Therefore, researchers are solving those issues by using Nature-Inspired Optimization Algorithms (NIOAs) as alternative methodologies for the multi-thresholding problem. This study presents an up-to-date review on all most important NIOAs employed in multi-thresholding based image segmentation domain. The key issues which are involved during the formulation of NIOAs based image multi-thresholding models are also discussed here.
Keywords:
ARTIFICIAL BEE COLONY
PARTICLE SWARM OPTIMIZATION
CUCKOO SEARCH ALGORITHM
NUMERICAL FUNCTION OPTIMIZATION
FLOWER POLLINATION ALGORITHM
META-HEURISTIC OPTIMIZATION
MOTH-FLAME OPTIMIZATION
MINIMUM CROSS-ENTROPY
GREY WOLF OPTIMIZER
DIFFERENTIAL EVOLUTION
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