基于视觉感知的模糊C均值聚类算法

A Visual Perception Based Fuzzy C-Means Clustering Algorithm

针对传统模糊C均值聚类算法对结构复杂图像分割效果不理想的问题,提出一种基于视觉感知的模糊C均值聚类算法.首先,在分析视皮层神经元感受野性质的基础上,建立视神经元细胞响应函数来计算图像的结构特征.其次,定义一种斜坡函数从仿生学的角度来模拟人眼对相对亮度变化的感知,用来计算图像中像素点与聚类中心点之间的差异.所提模型充分考虑了邻域刺激对中心神经元影响的方向性、位置相对性和周期性,比较精确地描述了图像的结构信息,有效地抑制了噪声和复杂纹理的干扰.实验结果表明,本文算法克服了传统模糊C均值聚类算法的缺点,实现了具有复杂背景图像的精确分割.

A visual perception based fuzzy C-means clustering algorithm was proposed to solve the problems that the results of complex structural image segmentation are not satisfactory.Firstly,on the basis of the receptive field properties analysis of neurons in the primary visual cortex,a visual nerve cell response function was proposed to calculate image structural feature.Secondly,a ramp function was used to simulate the visual perception of relative brightness change and calculate the difference between pixels in the image and the cluster centers.The relationship in direction,relative position and periodic between neighboring stimuli and the central neuron were fully considered,so the image structural information could be accurately described with the proposed model,and the noise and the complex texture interferences could be effectively suppressed.The experimental results demonstrated that the shortcoming of traditional fuzzy C-means clustering algorithm was overcome with the proposed algorithm,and accurate segmentation of complex background images was achieved.