计算机工程 ›› 2018, Vol. 44 ›› Issue (6): 226-232.doi: 10.19678/j.issn.1000-3428.0046669

• 图形图像处理 • 上一篇    下一篇

融合SLIC与改进邻近传播聚类的彩色图像分割算法

程仙国,王明军   

  1. 宁波工程学院 机械工程学院,浙江 宁波 315211
  • 收稿日期:2017-04-06 出版日期:2018-06-15 发布日期:2018-06-15
  • 作者简介:程仙国(1981—),男,讲师、博士,主研方向为图像分割、计算机视觉;王明军,副教授、博士。
  • 基金项目:
    浙江省自然科学基金(LY17F030006)。

Color Image Segmentation Algorithm Combining SLIC with Improved Affinity Propagation Clustering

CHENG Xianguo,WANG Mingjun   

  1. College of Mechanical Engineering,Ningbo University of Technology,Ningbo,Zhejiang 315211,China
  • Received:2017-04-06 Online:2018-06-15 Published:2018-06-15

摘要: 邻近传播(AP)聚类算法在分割彩色图像时,存在相似度矩阵计算规模大、聚类时间长、空间复杂度高等问题。为此,提出一种新的彩色图像分割算法。利用简单线性迭代聚类对彩色图像进行超像素预分割,计算各超像素的L、a和b颜色分量平均值,并根据颜色分量平均值间的负欧式距离构建AP聚类算法的相似度矩阵。在AP聚类迭代过程中给出一种参考度递减的改进聚类方法,提高AP聚类算法的精确性和鲁棒性,并运用超像素聚类的轮廓系数对其评价,获得最优的AP聚类结果。实验结果表明,与现有的彩色图像聚类分割方法相比,该算法分割效果和分割质量均有明显提高。

关键词: Lab颜色空间, 超像素, 邻近传播, 聚类, 彩色图像分割

Abstract: When Adjacent Propagation(AP) clustering algorithm is used to segment color images,the similarity matrix has the problems of large scale,long clustering time,and high space complexity.Therefore,a new color image segmentation algorithm is proposed.A Simple Linear Iterative Clustering(SLIC) is used to pre-segment the color image,the L,a and b color component averages of each superpixel are calculated,and the similarity matrix of the AP clustering algorithm is constructed according to the negative Euclidean distance between the color component averages.An improved clustering method with decreasing reference degree is proposed during the AP clustering iteration process to improve the accuracy and robustness of AP clustering algorithm.Super-pixel clustering is used to evaluate the contour coefficients to obtain the best AP clustering results.Experimental results show that compared with the existing color image clustering and segmentation methods,the segmentation effect and segmentation quality of the algorithm are significantly improved.

Key words: Lab color space, super pixel, Adjacent Propagation(AP), clustering, color image segmentation

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