Natural image segmentation algorithm with unsupervised FCM
无监督模糊C均值聚类自然图像分割算法

In this work, we propose a natural image segmentation method based on unsupervised fuzzy C-means (USFCM) clustering algorithm. The intersection of confidence intervals rules is utilized to adaptively compute the scale of Gabor filter for each pixel. Then image features are measured by Gabor filter with adaptively computed scale, orientation, frequency and phase. Meanwhile, a fast polynomial segmentation method is proposed to determine the number of clusters. Then the algorithm USFCM is utilized to get the final segmentation. The experimental results show that the proposed method can overcome the impact of texture and distinguish the target from background. The performances have demonstrated the effectiveness, accuracy and superiority of the proposed method.