Maximum Tsallis Entropy Thresholding for Image Segmentation Using a Refined Artificial Bee Colony Optimization

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In this paper to compute optimum thresholds for Maximum Tsallis entropy thresholding (MTET) model, a new hybrid algorithm is proposed by integrating the Artificial Bee Colony Optimization (ABC) with the Powell’s conjugate gradient (PCG) method. Here the ABC with improved perturbation mechanism (IPM) will act as the main optimizer for searching the near-optimal thresholds while the PCG method will be used to fine tune the best solutions obtained by the ABC in every iteration. This new multilevel thresholding technique is called the Refined Artificial Bee Colony Optimization (RABC) algorithm for MTET. Experimental results over multiple images with different range of complexities validate the efficiency of the proposed technique with regard to segmentation accuracy, speed, and robustness in comparison with other techniques reported in the literature. The experimental results demonstrate that the proposed RABC algorithm can search for multiple thresholds which are very close to the optimal ones examined by the exhaustive search method.
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Image Segmentation; Maximum Tsallis Entropy Thresholding; Artificial Bee Colony; Powell’s Conjugate Gradient Method

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