Pattern Recognition
A fast scheme for optimal thresholding using genetic algorithms
Signal Processing
Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm
Pattern Recognition Letters
Object segmentation using ant colony optimization algorithm and fuzzy entropy
Pattern Recognition Letters
Expert Systems with Applications: An International Journal
Computer Vision and Image Understanding
Optimal multi-level thresholding using a two-stage Otsu optimization approach
Pattern Recognition Letters
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
Engineering Applications of Artificial Intelligence
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
Modified bacterial foraging algorithm based multilevel thresholding for image segmentation
Engineering Applications of Artificial Intelligence
Median-based image thresholding
Image and Vision Computing
Applied Soft Computing
Computers & Mathematics with Applications
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
FSIM: A Feature Similarity Index for Image Quality Assessment
IEEE Transactions on Image Processing
An efficient method for segmentation of images based on fractional calculus and natural selection
Expert Systems with Applications: An International Journal
Image segmentation using Atanassov's intuitionistic fuzzy sets
Expert Systems with Applications: An International Journal
A multi-threshold segmentation approach based on Artificial Bee Colony optimization
Applied Intelligence
A survey of graph theoretical approaches to image segmentation
Pattern Recognition
| Hi-index | 12.05 |
The objective of image segmentation is to extract meaningful objects. A meaningful segmentation selects the proper threshold values to optimize a criterion using entropy. The conventional multilevel thresholding methods are efficient for bi-level thresholding. However, they are computationally expensive when extended to multilevel thresholding since they exhaustively search the optimal thresholds to optimize the objective functions. To overcome this problem, two successful swarm-intelligence-based global optimization algorithms, cuckoo search (CS) algorithm and wind driven optimization (WDO) for multilevel thresholding using Kapur's entropy has been employed. For this purpose, best solution as fitness function is achieved through CS and WDO algorithm using Kapur's entropy for optimal multilevel thresholding. A new approach of CS and WDO algorithm is used for selection of optimal threshold value. This algorithm is used to obtain the best solution or best fitness value from the initial random threshold values, and to evaluate the quality of a solution, correlation function is used. Experimental results have been examined on standard set of satellite images using various numbers of thresholds. The results based on Kapur's entropy reveal that CS, ELR-CS and WDO method can be accurately and efficiently used in multilevel thresholding problem.