Geometric consistent fuzzy cluster ensemble with membership reconstruction for image segmentation

In recent years, to handle the ubiquitous uncertainty and unknown noise, different fuzzy clustering methods have been introduced to solve image segmentation problem for various applications, such as natural image, satellite image, multichannel (typically remote sensing) image. In fact, it is hard to know which one is the best for specified task. Cluster ensemble method is proposed to solve the problem of choosing a particular fuzzy clustering algorithm, or a special image processing before clustering, that best suits the given image segmentation task. In fuzzy cluster ensemble, membership vectors generated from different fuzzy clustering methods are merged into one vector as an input object, which is also a combination of data partitions. However, this kind of input object may lose detail information from original target image and may cause inaccurate edges in segmentation results. Moreover, by means of treating it as a new representation of original data, the membership vector of fuzzy cluster ensemble should be intuitively geometric consistent with the original target image. In this paper, by holding this view, we develop a geometric consistent fuzzy cluster ensemble model for spatial data, which involves a constraint between the membership and its reconstruction, to improve the clustering performance on image segmentation. In the proposed model, a pre-determined gradient-preserving weight is used in the membership reconstruction item to make the membership matrix be geometric consistent with the original target image. A semi-implicit optimization iterative algorithm is adopted to solve the proposed geometric consistent model. Experimental results demonstrate the effectiveness of proposed model in synthetic and real-world image segmentation problems over several state-of-the-art methods.