Data clustering as an optimum-path forest problem with applications in image analysis

Authors:
Leonardo Marques Rocha;Fábio A. M. Cappabianco;Alexandre Xavier Falcão
Affiliations:
Department of Telecommunications, School of Electrical and Computer Engineering, University of Campinas, Brazil;Department of Information Systems, Institute of Computing, University of Campinas, Brazil;Department of Information Systems, Institute of Computing, University of Campinas, Brazil
Venue:
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Year:
2009

Citing 0
Cited 6

Precipitates segmentation from scanning electron microscope images through machine learning techniques

IWCIA'11 Proceedings of the 14th international conference on Combinatorial image analysis
A graph-based framework for thermal faceprint characterization

ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
A novel algorithm for feature selection using Harmony Search and its application for non-technical losses detection

Computers and Electrical Engineering
An Optimum-Path Forest framework for intrusion detection in computer networks

Engineering Applications of Artificial Intelligence
Brain tissue MR-image segmentation via optimum-path forest clustering

Computer Vision and Image Understanding
A data reduction and organization approach for efficient image annotation

Proceedings of the 28th Annual ACM Symposium on Applied Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.