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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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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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

CLC Number: 

  • TN911.73
[1] 贺飞跃,田铮,付辉敬,等. 带边缘惩罚和自适应权马尔科夫随机场的合成孔径雷达图像快速分割[J]. 光学学报, 2013, 33(8): 1-8.
He Fei-yue, Tian Zheng, Fu Hui-jing, et al. Models with edge penalties and an adaptive weighting parameter[J]. Acta Optica Sinica, 2013, 33(8): 1-8.
[2] 赵于前,杨元,王琨. 基于模糊集理论的迭代多值化图像分割[J]. 光电子激光, 2009, 20(10): 1403-1409.
Zhao Yu-qian, Yang Yuan, Wang Kun. Iterative multi-level image segmentation based on fuzzy set theory[J]. Journal of Optoelectronics Laser, 2009, 20(10): 1403-1409.
[3] 于海晶,李桂菊. 基于改进差分盒维数的烟雾分割方法[J]. 液晶与显示, 2013, 28(1): 115-119.
Yu Hai-jing, Li Gui-ju. Smoke segmentation method based improved differential box-counting fractal on dimension[J]. Chinese Journal of Liquid Crystals and Displays, 2013, 28(1): 115-119.
[4] 赵东, 赵宏伟, 于繁华. 动态多目标优化的运动物体图像分割[J].光学精密工程, 2015, 23(7): 2109-2116.
Zhao Dong, Zhao Hong-wei, Yu Fan-hua. Moving object image segmentation by dynamic multi-objective optimization[J]. Optics and Precision Engineering, 2015, 23(7): 2109-2116.
[5] 李晓峰,杨艳芳,秦勇. 基于小波域的模糊化区域竞争模型的图像分割方法[J]. 光电子激光, 2013, 24(2): 391-396.
Li Xiao-feng, Yang Yan-fang, Qin Yong. Image segmentation method based on fuzzy region competition model in wavelet domain[J]. Journal of Optoelectronics Laser, 2013, 24(2): 391-396.
[6] 郑欣,彭真明. 基于活跃度的脉冲耦合神经网络图像分割[J]. 光学精密工程, 2013, 21(3): 821-827.
Zheng Xin, Peng Zhen-ming. Image segmentation based on activity degree with pulse coupled neural networks[J]. Optics and Precision Engineering, 2013, 21(3): 821-827.
[7] 彭真明,蒋彪,肖峻.基于并行点火PCNN模型的图像分割新方法[J]. 自动化学报, 2008, 34(9): 1169-1173.
Peng Zhen-ming, Jiang Biao, Xiao Jun. A novel method of image segmentation based on parallelized firing PCNN[J]. Acta Automatica Sinica, 2008, 34(9): 1169-1173.
[8] Unnikrishnan R, Pantofaru C, Hebert M. Toward objective evaluation of image segmentation algorithms[J]. IEEE Trans PAMI, 2007, 29(6): 929-944.
[9] 刘春燕.图像分割评价方法研究[D]. 西安:西安电子科技大学电子工程学院, 2011.
Liu Chun-yan. Survey on evaluation methods of image segmentation algorithms[D]. Xi'an: School of Electronic Engineering,XiDian University, 2011.
[10] 黄锐,桑农,罗大鹏,等.融合感知一致程度的图像分割评价方法[J]. 华中技大学学报:自然科学版, 2010, 38(10): 52-56.
Huang Rui, Sang Nong, Luo Da-peng, et al. Method to evaluate image segmentation via fused perceptual consistency degree[J]. Journal of Huazhong University of Science and Technology (Natural Science Edition), 2010, 38(10): 52-56.
[11] Yang A, Wright J, Sastry S, et al. Unsupervised segmentation of natural images via lossy data compression[J]. Comput Vis Image Understand, 2008, 110(2): 212-225.
[12] Yin Peng-yeng. Multi-level minimum cross entropy threshold selection based on particle swam optimization[J]. Applied Mathematics and Computation, 2007, 184(2): 503-513.
[13] 张煜东,吴乐南.基于二维Tsallis熵的改进PCNN图像分割[J].东南大学学报:自然科学版, 2008, 38(4): 579-584.
Zhang Yu-dong, Wu Le-nan. Image segmentation based on 2D Tsallis entropy with improved pulse coupled neural networks[J]. Journal of Southeast University(Natural Science Edition),2008,38(4):579-584.
[14] Stewart R D, Fermin I, Opper M. Region growing with pulse-coupled neural networks: An alternative to seeded region growing[J]. IEEE Transaction on Neural Networks, 2002, 13(6): 1557-1562.
[15] 张健, 李宏升. 基于图论阈值算法的图像分割研究[J].液晶与显示, 2014,29(4): 592-597.
Zhang Jian, Li Hong-sheng. Image mosaic research based on wavelet and rough set algorithm[J]. Chinese Journal of Liquid Crystals and Displays,2014,29(4): 592-597.
[16] Diao W H, Mao X, Gui V. Metrics for performance evaluation of preprocessing algorithms in infrared small target images[J]. Progress in Electromagnetics Research, 2011, 115: 35-53.
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