A Watershed Image Segmentation Algorithm Based on Self-adaptive Marking and Interregional Affinity Propagation Clustering

CAI Qiang; LIU Ya-qi; CAO Jian; LI Hai-sheng; DU Jun-ping

doi:10.3969/j.issn.0372-2112.2017.08.015

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A Watershed Image Segmentation Algorithm Based on Self-adaptive Marking and Interregional Affinity Propagation Clustering

更新时间：2025-07-16

- A Watershed Image Segmentation Algorithm Based on Self-adaptive Marking and Interregional Affinity Propagation Clustering
- Acta Electronica Sinica Vol. 45, Issue 8, Pages: 1911-1918(2017)
- 作者机构：
  
  1. 北京工商大学计算机与信息工程学院,北京,100048
  2. 食品安全大数据技术北京市重点实验室,北京,100048
  3. 中国科学院信息工程研究所信息安全国家重点实验室,北京,100093
  4. 中国科学院大学网络空间安全学院,北京,100093
  5. 北京邮电大学计算机学院,北京,100876
  6. 北京工商大学计算机与信息工程学院,北京,100048
  7. 食品安全大数据技术北京市重点实验室,北京,100048
  8. 中国科学院信息工程研究所信息安全国家重点实验室,北京,100093
  9. 中国科学院大学网络空间安全学院,北京,100093
  10. 北京邮电大学计算机学院,北京,100876
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.0372-2112.2017.08.015
  CLC： TP391
- Published Online：25 August 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
CAI Qiang, LIU Ya-qi, CAO Jian, et al. A Watershed Image Segmentation Algorithm Based on Self-adaptive Marking and Interregional Affinity Propagation Clustering[J]. Acta Electronica Sinica, 2017, 45(8): 1911-1918.
DOI：

CAI Qiang, LIU Ya-qi, CAO Jian, et al. A Watershed Image Segmentation Algorithm Based on Self-adaptive Marking and Interregional Affinity Propagation Clustering[J]. Acta Electronica Sinica, 2017, 45(8): 1911-1918. DOI： 10.3969/j.issn.0372-2112.2017.08.015.

摘要

分水岭算法是一种高效的图像分割算法，能够准确地对图像进行基于区域的分割，但是存在易过分割的问题.为此本文提出一种改进的分水岭算法：首先，对彩色图像进行频谱包络滤波并计算彩色梯度获得梯度图像，再采取一种自适应设定参数的H-minima技术，对梯度图像的极小值区域进行标记；然后，对已标记极小值区域的梯度图像进行分水岭分割；最后，计算分水岭分割所得各区域的颜色矩，作为该区域的颜色特征，并对这些区域进行近邻传播聚类获得分割结果.通过与近年来其它改进的分水岭算法和采用聚类的图像分割算法实验比较，本文所提算法能更加有效地抑制过分割，提高分割准确率，具有良好的自适应性和鲁棒性.

Abstract

The watershed algorithm can conduct region-based image segmentation effectively and accurately

but it tends to cause over-segmentation.To tackle the above mentioned problem

an improved watershed algorithm is proposed

as follows:first of all

the color gradient is computed using spectrum envelope filtered color image

based on which

regions with minimum gradient are marked using self-adaptive H-minima transformation method.Then

the watershed transform is applied to segment the marked gradient image.Finally

affinity propagation clustering is adopted to merge the regions segmented by the watershed transform

using color moments computed on each local region

to get the final segmentation result.Experiments conducted on public available datasets demonstrate the adaptability and robustness of proposed algorithm

compared with the relative state-of-the-art methods.The proposed method can solve the over-segmentation problem well and get accurate results.

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