A fast scheme for optimal thresholding using genetic algorithms
Signal Processing
Image thresholding using Tsallis entropy
Pattern Recognition Letters
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Expert Systems with Applications: An International Journal
Analysis of the publications on the applications of particle swarm optimisation
Journal of Artificial Evolution and Applications - Regular issue
Solving shortest path problem using particle swarm optimization
Applied Soft Computing
An artificial bee colony algorithm for the leaf-constrained minimum spanning tree problem
Applied Soft Computing
A survey: algorithms simulating bee swarm intelligence
Artificial Intelligence Review
Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Solution to profit based unit commitment problem using particle swarm optimization
Applied Soft Computing
Application of particle swarm optimization to association rule mining
Applied Soft Computing
Multilevel image thresholding selection using the artificial bee colony algorithm
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
Artificial Bee Colony algorithm for optimization of truss structures
Applied Soft Computing
A modified Artificial Bee Colony (ABC) algorithm for constrained optimization problems
Applied Soft Computing
Modified bacterial foraging algorithm based multilevel thresholding for image segmentation
Engineering Applications of Artificial Intelligence
A modified Artificial Bee Colony algorithm for real-parameter optimization
Information Sciences: an International Journal
Optimal multilevel thresholding using bacterial foraging algorithm
Expert Systems with Applications: An International Journal
Image quality assessment: from error visibility to structural similarity
IEEE Transactions on Image Processing
Expert Systems with Applications: An International Journal
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Segmentation is a critical task in image processing. Bi-level segmentation involves dividing the whole image into partitions based on a threshold value, whereas multilevel segmentation involves multiple threshold values. A successful segmentation assigns proper threshold values to optimise a criterion such as entropy or between-class variance. High computational cost and inefficiency of an exhaustive search for the optimal thresholds leads to the use of global search heuristics to set the optimal thresholds. An emerging area in global heuristics is swarm-intelligence, which models the collective behaviour of the organisms. In this paper, two successful swarm-intelligence-based global optimisation algorithms, particle swarm optimisation (PSO) and artificial bee colony (ABC), have been employed to find the optimal multilevel thresholds. Kapur's entropy, one of the maximum entropy techniques, and between-class variance have been investigated as fitness functions. Experiments have been performed on test images using various numbers of thresholds. The results were assessed using statistical tools and suggest that Otsu's technique, PSO and ABC show equal performance when the number of thresholds is two, while the ABC algorithm performs better than PSO and Otsu's technique when the number of thresholds is greater than two. Experiments based on Kapur's entropy indicate that the ABC algorithm can be efficiently used in multilevel thresholding. Moreover, segmentation methods are required to have a minimum running time in addition to high performance. Therefore, the CPU times of ABC and PSO have been investigated to check their validity in real-time. The CPU time results show that the algorithms are scalable and that the running times of the algorithms seem to grow at a linear rate as the problem size increases.