A survey of thresholding techniques
Computer Vision, Graphics, and Image Processing
Optimal thresholding—a new approach
Pattern Recognition Letters
Automatic threshold selection based on histogram modes and a discriminant criterion
Machine Vision and Applications
Digital Image Processing
Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures
Neural Computation
A Novel Image Segmentation Approach Based on Particle Swarm Optimization
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
State-Space Models: From the EM Algorithm to a Gradient Approach
Neural Computation
On the performance of artificial bee colony (ABC) algorithm
Applied Soft Computing
Multilevel thresholding for image segmentation through a fast statistical recursive algorithm
Pattern Recognition Letters
On convergence properties of the em algorithm for gaussian mixtures
Neural Computation
Motion detection and object tracking with discrete leaky integrate-and-fire neurons
Applied Intelligence
Singularity and Slow Convergence of the EM algorithm for Gaussian Mixtures
Neural Processing Letters
Structural inverse analysis by hybrid simplex artificial bee colony algorithms
Computers and Structures
An artificial bee colony approach for clustering
Expert Systems with Applications: An International Journal
A novel clustering approach: Artificial Bee Colony (ABC) algorithm
Applied Soft Computing
Systematic image quality assessment for sewer inspection
Expert Systems with Applications: An International Journal
A discrete artificial bee colony algorithm for the lot-streaming flow shop scheduling problem
Information Sciences: an International Journal
Recognition of Arabic (Indian) bank check digits using log-gabor filters
Applied Intelligence
On searching for an optimal threshold for morphological image segmentation
Pattern Analysis & Applications
A target-based color space for sea target detection
Applied Intelligence
Artificial bee colony algorithm and pattern search hybridized for global optimization
Applied Soft Computing
Adaptive cooperative particle swarm optimizer
Applied Intelligence
Expert Systems with Applications: An International Journal
Dynamic bee colony algorithm based on multi-species co-evolution
Applied Intelligence
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This paper explores the use of the Artificial Bee Colony (ABC) algorithm to compute threshold selection for image segmentation. ABC is an evolutionary algorithm inspired by the intelligent behavior of honey-bees which has been successfully employed to solve complex optimization problems. In this approach, an image 1-D histogram is approximated through a Gaussian mixture model whose parameters are calculated by the ABC algorithm. In the model, each Gaussian function represents a pixel class and therefore a threshold point. Unlike the Expectation-Maximization (EM) algorithm, the ABC method shows fast convergence and low sensitivity to initial conditions. Remarkably, it also improves complex time-consuming computations commonly required by gradient-based methods. Experimental results over multiple images with different range of complexity validate the efficiency of the proposed technique with regard to segmentation accuracy, speed, and robustness. The paper also includes an experimental comparison to the EM and to one gradient-based method which ultimately demonstrates a better performance from the proposed algorithm.