We discuss a novel statistical framework for image segmentation based on nonparametric clustering. By employing the mean shift procedure for analysis, image regions are identified as clusters in the joint color-spatial domain. To measure the significance of each cluster we use a test statistics that compares the estimated density of the cluster mode with the estimated density on the cluster bou...

Clustering or cluster analysis [1] is a classical method in unsupervised learning and one of the most used techniques in statistical data analysis. Clustering has a wide range of applications in many areas like pattern recognition, medical diagnostics, data mining, biology, market research and image analysis among others. A cluster is a set of data points that in some sense are similar to each ...

‎In some fields‎, ‎there is an interest in distinguishing different geometrical objects from each other‎. ‎A field of research that studies the objects from a statistical point of view‎, ‎provided they are‎ ‎invariant under translation‎, ‎rotation and scaling effects‎, ‎is known as the statistical shape analysis‎. ‎Having some objects that are registered using key points on the outline...

We present a domain-independent clusteringbased approach for automatic extraction of multiword expressions (MWEs). The method combines statistical information from a general-purpose corpus and texts from Wikipedia articles. We incorporate association measures via dimensions of data points to cluster MWEs and then compute the ranking score for each MWE based on the closest exemplar assigned to a...