基于免疫克隆選擇算法的圖像分割
Application of Immune Clone Selection Algorithm to Image Segmentation
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摘要: 圖像分割是圖像處理領域中不可缺少的一個分支。該文基于閾值分割方法，將免疫克隆選擇優(yōu)化算法應用到圖像分割中，提出了一種新的圖像分割算法。詳細敘述了算法機理，并對算法復雜度進行了理論分析以及實驗數(shù)據(jù)比較。在仿真實驗中，將遺傳算法和免疫克隆選擇算法分別獨立運行10次，對10次得到的閾值以及均值、方差進行了比較，并將函數(shù)評價次數(shù)作為算法復雜度的評價指標。該文算法不僅能夠對圖像進行準確的分割，而且在同樣的種群規(guī)模下能夠以較少的迭代代數(shù)和較低的函數(shù)評價次數(shù)得到最優(yōu)閾值。仿真結果表明，該方法應用在圖像分割中是可行的、有效的。Abstract: Image segmentation is a significant part in image processing field. Inspired by the threshold-based segmentation methods, a novel algorithm based on immune clone selection and optimal entropy theory is presented in this paper. Immune clone selection algorithm performs not only local but also global search, and has better performance than Genetic Algorithm(GA) in searching for the optimal entropy threshold of images. The algorithm is depicted in detail and the computational complexity is given. In experiments, natural image and SAR image are selected, and the algorithm runs ten times independently and the mean numbers of function values are presented as the evaluation of the algorithm complexity. It shows that the algorithm presented in this paper can find better solutions with small generation and mean numbers of function values. So this method has better performance in stabilization and convergence than GA. Experimental results show that this method is feasible and effective.
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