Multi-objective Evolutionary Semi-supervised Fuzzy Clustering Image Segmentation Motivated by Region Information
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摘要:
现有的多目标进化聚类算法应用于图像分割时，往往是在图像像素层面上进行聚类，运行时间过长，而且忽略了图像区域信息使得图像分割效果不太理想。为了提高多目标进化聚类算法的分割效果和时间效率，该文将图像区域信息与部分监督信息引入多目标进化聚类，提出图像区域信息驱动的多目标进化半监督模糊聚类图像分割算法。该算法首先利用超像素策略获得图像的区域信息，然后结合部分监督信息，设计融合区域信息和监督信息的适应度函数，接着通过多目标进化策略对多个适应度函数进行优化得到最优解集。最后构造融合区域信息与监督信息的最优解评价指标，实现从最优解集中选取一个最优解。实验结果表明：与已有多目标进化聚类算法相比，该算法不但分割效果有所提升，而且运行效率得以提高。
Abstract:When multi-objective evolutionary clustering algorithms are applied to image segmentation, the image pixels are always utilized to be clustered. It results in a long running time. In addition, due to not considering the image region information, the image segmentation effect is not ideal. In order to improve the segmentation effect and time efficiency of the multi-objective evolutionary clustering algorithm, the image region information and some supervised information are introduced into multi-objective evolutionary clustering. Then a multi-objective evolutionary semi-supervised fuzzy clustering image segmentation algorithm driven by image region information is presented. First, the region information of the image is obtained through the super-pixel strategy. Second, two novel fitness functions are designed by introducing the supervised information and region information. Third, the multi-objective evolutionary strategy is used to optimize these two fitness functions to obtain an optimal solution set. Finally, an optimal solution evaluation index with region information and supervision information is constructed and utilized to select an optimal solution from the optimal solution set. Experimental results show the proposed algorithm outperforms comparison methods in segmentation performance and running efficiency.
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表 1 各算法聚类准确率对比
图像 FCM SSFCM SSFC-SC MOVGA 本文算法 #3096 0.9859 0.9860 0.9865 0.5388 0.9931 #135069 0.7368 0.9926 0.9924 0.3301 0.9925 #118035 0.9342 0.9342 0.9337 0.9367 0.9523 #124084 0.7415 0.7418 0.8465 0.8678 0.9457 #86016 0.8394 0.8395 0.8568 0.6190 0.9811 #161062 0.8846 0.8847 0.8988 0.5711 0.9830 #260058 0.7893 0.7898 0.8301 0.3730 0.9904 #8068 0.9517 0.9518 0.9518 0.7112 0.9858 #113044 0.8381 0.8384 0.8395 0.2664 0.9330 #12003 0.7737 0.7735 0.8079 0.4421 0.8919 #296059 0.7397 0.7396 0.7400 0.6364 0.9284 #238011 0.8093 0.9565 0.9565 0.9566 0.9605 #101027 0.8839 0.8840 0.8850 0.5689 0.9024 #28075 0.4479 0.4456 0.5666 0.5873 0.9374 #24063 0.9675 0.9675 0.9696 0.9601 0.9737 #253036 0.6193 0.6195 0.6921 0.6443 0.9448 #42044 0.7524 0.7526 0.7572 0.7055 0.8595 #299091 0.6962 0.6964 0.7220 0.3360 0.9564 #113016 0.8164 0.8142 0.8843 0.7203 0.9426 #147091 0.9316 0.9317 0.9314 0.7781 0.9041 
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