Spatially chunking support vector clustering algorithm

We propose a novel spatially chunking algorithm to speed up the support vector clustering (SVC) method for large data sets. The input data set is first divided into subsets where samples are geometrically adjacent to each other, an SVC is trained for each subset, and finally the clustering results of the local SVCs are combined to yield a global clustering solution. This method can save the computation cost for SVC by breaking the quadratic programming problem into smaller ones, and since parameter selection is done for each subset, it is able to deal with unevenly distributed data sets. The proposed method has demonstrated satisfactory performance with image segmentation problems on both gray scale and color images.