Spectral clustering enjoys its success in both data clustering and semisupervised learning. But, most spectral clustering algorithms cannot handle multi-class clustering problems directly. Additional strategies are needed to extend spectral clustering algorithms to multi-class clustering problems. Furthermore, most spectral clustering algorithms employ hard cluster membership, which is likely t...

We present a novel spectral clustering method that enables users to incorporate prior knowledge of the size of clusters into the clustering process. The cost function, which is named size regularized cut (SRcut), is defined as the sum of the inter-cluster similarity and a regularization term measuring the relative size of two clusters. Finding a partition of the data set to minimize SRcut is pr...

• Presented an algorithm that learns and helps users visualize important clustering dimensions of a dataset. • Future work involves quantifying the multi-clusterability and ambiguity of a dataset Conclusion and Future Work • Producing the optimal clustering • Spectral clustering, objective function: normalized cut • Optimal partitioning function f: • f=e2, the second eigenvector of the Laplacia...

Minimum spanning tree and normalized cut based segmentation method are evaluated with respect to segmentations made by humans. The segmentation error is determined by two discrepancy measures, as a sum of miss-counted segmented pixels. Gray value images are used for the evaluation. This benchmarking is well suited in finding the class of images best suited for a particular method.

Spectral clustering is based on the spectral relaxation of the normalized/ratio graph cut criterion. While the spectral relaxation is known to be loose, it has been shown recently that a non-linear eigenproblem yields a tight relaxation of the Cheeger cut. In this paper, we extend this result considerably by providing a characterization of all balanced graph cuts which allow for a tight relaxat...

We consider the task of unsupervised lecture segmentation. We formalize segmentation as a graph-partitioning task that optimizes the normalized cut criterion. Our approach moves beyond localized comparisons and takes into account longrange cohesion dependencies. Our results demonstrate that global analysis improves the segmentation accuracy and is robust in the presence of speech recognition er...

Image segmentation using graph cuts have become very popular in the last years. These methods are computationally expensive, even with hard constraints (seed pixels). We present a solution that runs in time proportional to the number of pixels. Our method computes an ordered region growing from a set of seeds inside the object, where the propagation order of each pixel is proportional to the co...

Individual cells play essential roles in the biological processes of the brain. The number of neurons changes during both normal development and disease progression. High-resolution imaging has made it possible to directly count cells. However, the automatic and precise segmentation of touching cells continues to be a major challenge for massive and highly complex datasets. Thus, an integrative...

This is a survey of the method of graph cuts and its applications to graph clustering of weighted unsigned and signed graphs. I provide a fairly thorough treatment of the method of normalized graph cuts, a deeply original method due to Shi and Malik, including complete proofs. I also cover briefly the method of ratio cuts, and show how it can be viewed as a special case of normalized cuts. I in...

Automation of agricultural harvesting equipment in the near term appears both economically viable and technically feasible. This paper describes a vision-based algorithm which guides a harvester by tracking the line between cut and uncut crop. Using this algorithm, a harvester has successfully cut roughly one acre of crop to date, at speeds of up to 4.5 miles an hour, in an actual alfalfa field...