On combining graph-partitioning with non-parametric clustering for image segmentation

The goal of this communication is to suggest an alternative implementation of the k-way Ncut approach for image segmentation. We believe that our implementation alleviates a problem associated with the Ncut algorithm for some types of images: its tendency to partition regions that are nearly uniform with respect to the segmentation parameter. Previous implementations have used the k-means algorithm to cluster the data in the eigenspace of the affinity matrix. In the k-means based implementations, the number of clusters is estimated by minimizing a function that represents the quality of the results produced by each possible value of k. Our proposed approach uses the clustering algorithm of Koontz and Fukunaga in which k is automatically selected as clusters are formed (in a single iteration). We show comparison results obtained with the two different approaches to non-parametric clustering. The Ncut generated oversegmentations are further suppressed by a grouping stage—also Ncut based—in our implementation. The affinity matrix for the grouping stage uses similarity based on the mean values of the segments.