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基于PSO與K-均值算法的農業(yè)超綠圖像分割方
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Extra-green Image Segmentation Based on Particle Swarm Optimization and K-means Clustering
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    摘要:

    為了解決K-均值算法對農業(yè)圖像中常用的超綠特征2G—R—B圖像分割效果不佳的缺點,，提出一種基于微粒群與K均值算法的圖像分割方法,。先用K均值算法對圖像進行快速分類,，然后將分類結果作為其中一個微粒的結果，利用微粒群算法計算,，最后用K-均值算法在新的分類基礎上計算新的聚類中心，更新當前的位置,，以得到最優(yōu)的圖像分割閾值,。試驗結果表明，改進算法對超綠特征2G—R—B圖像能夠準確分割目標,，且對不同類型的農業(yè)超綠圖像具有較好的適應性,。

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    In order to solve the disadvantage of image segmentation by K-means clustering to extra-green character used to be adopted in agricultural images, an image segmentation method based on the particle swarm optimization and the K means clustering was proposed. Firstly, image pixels value was fast clustered with the K-means clustering. Regarding the results as the position of a particle, PSO can be used and the new class centers also can be re-calculated with the K means clustering. Subsequently,the position of all particles got updated and the optimal threshold was obtained. Experimental results proved that the improved algorithm was an effective method for segmenting the object accurately from images, and applicable for various kinds of agricultural images with extra-green character.

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趙博,宋正河,毛文華,毛恩榮,張小超.基于PSO與K-均值算法的農業(yè)超綠圖像分割方[J].農業(yè)機械學報,2009,40(8):166-169. Extra-green Image Segmentation Based on Particle Swarm Optimization and K-means Clustering[J]. Transactions of the Chinese Society for Agricultural Machinery,2009,40(8):166-169.

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