吉林大学学报(工学版) ›› 2016, Vol. 46 ›› Issue (1): 311-317.doi: 10.13229/j.cnki.jdxbgxb201601047

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基于活跃度的图像分割算法性能评价新方法

郑欣, 彭真明, 邢艳   

  1. 电子科技大学 光电信息学院, 四川 成都 610054
  • 收稿日期:2014-05-20 出版日期:2016-01-30 发布日期:2016-01-30
  • 作者简介:郑欣(1981-),男,博士研究生.研究方向:数字图像处理、目标识别与跟踪.E-mail:zheng_xin2@sina.com
  • 基金资助:
    国家自然科学基金项目(61308102); 中国科学院光束控制重点实验室基金项目(2010LBC001)

Novel method of evaluating image segmentation algorithms based on activity degree

ZHENG Xin, PENG Zhen-ming, XING Yan   

  1. School of Opto-Electronic Information, University of Electronic Science and Technology of China, Chengdu 610054, China
  • Received:2014-05-20 Online:2016-01-30 Published:2016-01-30

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摘要： 针对优度法在评价图像分割算法性能时忽略了图像质量对算法性能影响这一不足,分析了图像中影响分割算法性能的因素,并提出用图像活跃度定量描述上述因素。在此基础上,构造了一种基于图像质量的分割算法性能评价方法,即加权优度法。实验表明,加权优度法对分割结果的评价值与主观评价的一致性程度比优度法更高,评价结果与图像质量间的单调性也更好,证明了本文方法的有效性。

关键词: 信息处理技术, 图像分割, 性能评价, 图像活跃度, 加权优度法

Abstract: The excellent degree method is widely used in performance evaluation of image segmentation algorithms. However, this method ignores the influence of image quality. Hence, the results have no reference value to the overall performance evaluation of the segmentation algorithm or the comparison of different segmentation algorithms. To solve this problem, the image factors interfering with the performance of the segmentation algorithms were analyzed. Then, the metric Activity Degree of Image (ADI) was proposed to quantify these factors. Finally, based on the ADI, a performance evaluation method of the image segmentation algorithms was developed, named Weighted Excellent Degree (WED) method. Experiment results show that, compared with the current evaluation method, the WED method has higher consistency degree between evaluation results and subjective assessment, and better monotonicity between evaluation results and image qualities. Thus, the proposed WED method is more effective in performance evaluation of image segmentation algorithms than the current evaluation method.

Key words: information processing, image segmentation, performance evaluation, activity degree of image, weighted excellent degree

中图分类号: 

  • TN911.73
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