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Data clustering as an optimum‐path forest problem with applications in image analysis
Author(s) -
Rocha Leonardo Marques,
Cappabianco Fábio A. M.,
Falcão Alexandre Xavier
Publication year - 2009
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.20191
Subject(s) - cluster analysis , adjacency list , computer science , path (computing) , graph , tree (set theory) , pattern recognition (psychology) , segmentation , image (mathematics) , relation (database) , artificial intelligence , minimum spanning tree , probability density function , data mining , adjacency matrix , path length , algorithm , mathematics , theoretical computer science , combinatorics , statistics , computer network , programming language
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 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. We also include experimental comparisons with other clustering approaches. © 2009 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 19, 50–68, 2009.