A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Clustering is inherently a difficult problem, both with respect to the definition of adequate models as well as to the optimization of the models. We present a model for the cluster problem that does not need knowledge about the number of clusters a priori . This property is among others useful in the image segmentation domain, which we especially address. Further, we propose a cellular coevolutionary algorithm for the optimization of the model. Within this scheme multiple agents are placed in a regular two-dimensional (2-D) grid representing the image, which imposes neighboring relations on them. The agents cooperatively consider pixel migration from one agent to the other in order to improve the homogeneity of the ensemble of the image regions they represent. If the union of the regions of neighboring agents is homogeneous then the agents form alliances. On the other hand, if an agent discovers a deviant subject, it isolates the subject. In the experiments we show the effectiveness of the proposed method and compare it to other segmentation algorithms. The efficiency can easily be improved by exploiting the intrinsic parallelism of the proposed method.