Particle Competitive Mechanism Based Multiobjective Rough Clustering Algorithm for Image Segmentation

Rough clustering has attracted increasing attention due to well dealing with the fuzziness and uncertainty of data. It is well known that it needs to manually set the threshold to determine the upper and lower approximations of rough clusters, which may bring a great effect on the clustering performance. When applied to image segmentation, rough clustering is always sensitive to the initialized cluster centers and image noise. Furthermore, only one clustering criterion is considered in rough clustering, which cannot satisfy diverse practical requirements. To handle these issues, a particle competitive mechanism based multiobjective rough clustering algorithm (PCM-MORCA) for image segmentation is proposed. First, a rough intraclass compactness function considering the nonlocal spatial information derived from an image is constructed to overcome the sensitivity to image noise. Next, the constructed rough intraclass compactness function and an interclass separation function are optimized simultaneously to make cluster centers meet diverse segmentation requirements. Then, an adaptive threshold determination mechanism by which the threshold adaptively varies with the clustered data is presented to well determine the upper and lower approximations of rough clusters. After that, to effectively search appropriate cluster centers, a novel pair competition-based particle weight updating strategy is designed for multiobjective particle swarm optimization by improving the elite particle selection and particle update. Finally, a rough clustering index with the nonlocal spatial information is constructed for selecting the optimal solution for PCM-MORCA. Segmentation experiments on Berkeley and magnetic resonance images reveal that PCM-MORCA behaves well on the segmentation accuracy and noise robustness.