Multilevel Thresholding Color Image Segmentation Using a Modified Artificial Bee Colony Algorithm

Sipeng ZHANG  Wei JIANG  Shin'ichi SATOH  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.8   pp.2064-2071
Publication Date: 2018/08/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2017EDP7183
Type of Manuscript: PAPER
Category: Artificial Intelligence, Data Mining
Keyword: 
color image segmentation,  multilevel thresholding method,  artificial bee colony algorithm,  krill herd algorithm,  

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Summary: 
In this paper, a multilevel thresholding color image segmentation method is proposed using a modified Artificial Bee Colony(ABC) algorithm. In this work, in order to improve the local search ability of ABC algorithm, Krill Herd algorithm is incorporated into its onlooker bees phase. The proposed algorithm is named as Krill herd-inspired modified Artificial Bee Colony algorithm (KABC algorithm). Experiment results verify the robustness of KABC algorithm, as well as its improvement in optimizing accuracy and convergence speed. In this work, KABC algorithm is used to solve the problem of multilevel thresholding for color image segmentation. To deal with luminance variation, rather than using gray scale histogram, a HSV space-based pre-processing method is proposed to obtain 1D feature vector. KABC algorithm is then applied to find thresholds of the feature vector. At last, an additional local search around the quasi-optimal solutions is employed to improve segmentation accuracy. In this stage, we use a modified objective function which combines Structural Similarity Index Matrix (SSIM) with Kapur's entropy. The pre-processing method, the global optimization with KABC algorithm and the local optimization stage form the whole color image segmentation method. Experiment results show enhance in accuracy of segmentation with the proposed method.