Reference TypeConference Proceedings
Citation KeyMonteroFalc:2018:DiClAp
Author1 Montero, Adán Echemendía
2 Falcão, Alexandre Xavier
Affiliation1 Laboratory of Image Data Science, Institute of Computing, University of Campinas
2 Laboratory of Image Data Science, Institute of Computing, University of Campinas
TitleA Divide-and-Conquer Clustering Approach based on Optimum-Path Forest
Conference NameConference on Graphics, Patterns and Images, 31 (SIBGRAPI)
EditorRoss, Arun
Gastal, Eduardo S. L.
Jorge, Joaquim A.
Queiroz, Ricardo L. de
Minetto, Rodrigo
Sarkar, Sudeep
Papa, João Paulo
Oliveira, Manuel M.
Arbeláez, Pablo
Mery, Domingo
Oliveira, Maria Cristina Ferreira de
Spina, Thiago Vallin
Mendes, Caroline Mazetto
Costa, Henrique Sérgio Gutierrez
Mejail, Marta Estela
Geus, Klaus de
Scheer, Sergio
Book TitleProceedings
DateOct. 29 - Nov. 1, 2018
Publisher CityLos Alamitos
PublisherIEEE Computer Society
Conference LocationFoz do Iguaçu, PR, Brazil
Keywordsclustering, optimum-path forest, image segmentation, image foresting transform, divide-and-conquer.
AbstractData clustering is one of the main challenges when solving Data Science problems. Despite its progress over almost one century of research, clustering algorithms still fail in identifying groups naturally related to the semantics of the problem. Moreover, the technological advances add crucial challenges with a considerable data increase, which are not handled by most techniques. We address these issues by proposing a divide-and-conquer approach to a clustering technique, which is unique in finding one group per dome of the probability density function of the data --- the Optimum-Path Forest (OPF) clustering algorithm. Our approach can use all samples, or at least many samples, in the unsupervised learning process without affecting the grouping performance and, therefore, being less likely to lose relevant grouping information. We show that it can obtain satisfactory results when segmenting natural images into superpixels.
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Access Date2020, Nov. 29