结合形态学重建和超像素的多特征FCM分割算法  

Multi-Feature FCM Segmentation Algorithm Combining Morphological Reconstruction and Superpixels

作　　者：马喃喃 刘丛[1] MA Nan-Nan;LIU Cong(School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《计算机系统应用》2021年第2期194-200,共7页Computer Systems & Applications

基　　金：国家自然科学基金(61703278)。

摘　　要：针对现有模糊聚类分割算法对噪声的鲁棒性差且提取的图像特征不充分等问题,本文提出了一种结合形态学重建和超像素的多特征模糊C-均值(FCM)分割算法.首先,利用形态学闭合重建处理原图像,提高了算法的鲁棒性和细节保护能力.其次,采用Mean-Shift方法预分割重建图像,获得一组超像素区域.再次,提取重建图像各像素的颜色特征、纹理特征和梯度特征,利用平均策略定义各超像素的颜色特征、纹理特征和梯度特征,组成多维特征向量.最后,运用最大熵正则化的加权模糊C-均值算法(EWFCM)的框架,以超像素为单位,以核诱导距离作为距离度量来聚类多维特征向量.选取BSDS300数据集中的6幅图像完成实验对比.结果表明,本文算法具有更高的分割精度.Aiming at the problems in the existing fuzzy clustering segmentation algorithms,such as poor noise robustness and insufficient image feature extraction,we propose a multi-feature FCM segmentation algorithm combining morphological reconstruction and superpixels.First,the original image is subject to morphological closing reconstruction,which improves the robustness and detail-preserving ability of the algorithm.Secondly,the mean-shift method is employed to pre-segment the reconstructed image and obtain a set of superpixels.Thirdly,the color,texture and gradient features of each superpixel in the reconstructed image are extracted and defined by an averaging strategy to form the multi-dimensional feature vectors.Finally,these vectors are clustered by using the framework of the EWFCM algorithm,taking superpixels as the unit and the nuclear induced distance as the distance measure.Furthermore,six images in the BSDS300 data set are selected for the experimental comparison.The results show that the algorithm in this study has higher segmentation accuracy.

关 键 词：形态学重建 超像素 MEAN-SHIFT EWFCM 核诱导距离 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]