Data Clustering as an Optimum-Path Forest Problem with Applications in Image Analysis

We propose an approach for data clustering based on optimum-path forest. The samples are taken as nodes of a graph, whose arcs are defined by an adjacency relation. The nodes are weighted by their probability density values (pdf) and a \emph{connectivity function} is maximized, such that each maximum of the pdf becomes root of an optimum-path tree (cluster), composed by samples ``more strongly connected'' to that maximum than to any other root. We discuss the advantages over other pdf-based approaches and present extensions to large datasets with results for interactive image segmentation and for fast, accurate, and automatic brain tissue classification in magnetic resonance (MR) images.

2008