自适应属性加权2维FCM分割算法

Adaptive weighted two-dimensional histogram FCM segmentation algorithm

目的为了提高2维直方图模糊C均值聚类分割算法的抗噪性和普适性,提出了属性加权2维直方图模糊C均值聚类分割新方法。方法针对2维直方图模糊C均值聚类分割算法存在阈值参数选取不当导致抗噪性能差的不足,将属性加权引入2维直方图模糊C均值聚类并有效解决了每维属性聚类贡献度的问题。结果本文算法相比2维直方图模糊C均值聚类分割法抗椒盐和高斯噪声性能平均提高了2 3 d B;同时,相比模糊局部C均值聚类分割法抗椒盐噪声性能平均提高了2 3 d B且抗高斯噪声性能稍差大约1 d B,但本文算法相比模糊局部C均值聚类分割法的速度平均提高了大约40倍。结论实验结果表明,本文算法相比现有2维直方图模糊C均值聚类算法更适合噪声图像分割;同时,相比模糊局部C均值聚类算法更有利于实时性要求较高场合的目标跟踪和识别等需要。同时从大量图像测试得出,本文算法对于一般人工合成图像、智能交通图像及遥感图像等具有普遍适用性。

Objective To improve the noise immunity and universality of the fuzzy C-means clustering segmentation algorithm based on a two-dimensional histogram, we propose a weighted fuzzy C-means clustering segmentation method on the basis of a dimensional histogram. Method The threshold parameter selection inherent in the fuzzy C-means clustering segmentation algorithm based on a two-dimensional histogram leads to poor noise immunity. This issue is addressed in this work with the introduction of weighting properties for the weighted fuzzy C-means clustering segmentation method based on a two-dimensional histogram. This approach is an effective solution for each dimension of the attributes of the poly problem class contribution. Result Compared with the algorithm based on a two-dimensional histogram, the proposed algorithm shows an average increase of 2 dB to 3 dB in its salt and pepper and Gaussian noise immunity. The same is true for the proposed algorithm when compared with the C-means clustering segmentation algorithm based on fuzzy local information. In the latter comparison, the proposed method reduces its anti-Gaussian noise to less than 1 dB and is 40 times slower than the C- means clustering segmentation a/gorithrn based on fuzzy local information. Conclusion The proposed method more effectively addresses noisy image segmentation requirements compared with the existing fuzzy C-means clustering algorithm based on a two-dimensional histogram. Moreover, the proposed method is more applicable in target tracking occasions and identification than the fuzzy C-means clustering algorithm based on fuzzy local information. At the same time, a large number of tests proved that the proposed algorithm is suitable for the synthetic images, intelligent traffic images and remote sensing image.