Pattern Recognition
A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Automatic thresholding of gray-level pictures using two-dimensional entropy
Computer Vision, Graphics, and Image Processing
Performance study of several global thresholding techniques for segmentation
Computer Vision, Graphics, and Image Processing
Optimal multi-thresholding using a hybrid optimization approach
Pattern Recognition Letters
Multilevel Minimum Cross Entropy Threshold Selection Based on Quantum Particle Swarm Optimization
SNPD '07 Proceedings of the Eighth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing - Volume 02
Expert Systems with Applications: An International Journal
Automatic Threshold Selection Based on Particle Swarm Optimization Algorithm
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 01
Nature-Inspired Metaheuristic Algorithms
Nature-Inspired Metaheuristic Algorithms
Multilevel minimum cross entropy threshold selection based on the honey bee mating optimization
Expert Systems with Applications: An International Journal
Pattern Recognition Letters
Firefly algorithms for multimodal optimization
SAGA'09 Proceedings of the 5th international conference on Stochastic algorithms: foundations and applications
A novel chemistry based metaheuristic optimization method for mining of classification rules
Expert Systems with Applications: An International Journal
Multimodal size, shape, and topology optimisation of truss structures using the Firefly algorithm
Advances in Engineering Software
Hi-index | 12.05 |
The minimum cross entropy thresholding (MCET) has been widely applied in image thresholding. The search mechanism of firefly algorithm inspired by the social behavior of the swarms of firefly and the phenomenon of bioluminescent communication, is used to search for multilevel thresholds for image segmentation in this paper. This new multilevel thresholding algorithm is called the firefly-based minimum cross entropy thresholding (FF-based MCET) algorithm. Four different methods that are the exhaustive search, the particle swarm optimization (PSO), the quantum particle swarm optimization (QPSO) and honey bee mating optimization (HBMO) methods are implemented for comparison with the results of the proposed method. The experimental results show that the proposed FF-based MCET algorithm can efficiently search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method when the number of thresholds is less than 5. The need of computation time of using the FF-based MCET algorithm is the least, meanwhile, the results using the FF-based MCET algorithm is superior to the ones of PSO-based and QPSO-based MCET algorithms but is not significantly different to the HBMO-based MCET algorithm.