In this work we propose a novel geometric clustering algorithm based on the Tensor Voting Framework (TVF). More precisely, we propose the construction of a weighted graph by means of the information diffused by TVF during the vote casting step. This graph, which summarizes informations related to the manifold geometric structure, was used for clustering purposes. To this aim, we applied the well known Dijkstra and Ford Fulkerson algorithms to recursively separate weakly connected graph components. We performed preliminary tests, comparing our algorithm with that obtained by employing a weighted version of the -NN graph. The obtained results on both synthetic and real data show that the proposed technique is promising. To test our algorithm on real datasets, we preprocessed graylevel input images by extracting their edge pixel points.
A novel approach for geometric clustering based on tensor voting framework / G. Lombardi, A. Rozza, E. Casiraghi, P. Campadelli (FRONTIERS IN ARTIFICIAL INTELLIGENCE AND APPLICATIONS). - In: Neural nets wirn11 : proceedings of the 21st italian workshop on neural nets / [a cura di] B. Apolloni, S. Bassis, A. Esposito, C.F. Morabito. - Washington : IOS press, 2011 Jun 03. - ISBN 978-1-60750-971-4. - pp. 129-138 (( Intervento presentato al 21. convegno Italian Workshop on Neural Networks tenutosi a Vietri sul Mare nel 2011.
|Titolo:||A novel approach for geometric clustering based on tensor voting framework|
|Parole Chiave:||tensor voting framework ; geometric clustering ; manifold learning|
|Settore Scientifico Disciplinare:||Settore INF/01 - Informatica|
|Data di pubblicazione:||3-giu-2011|
|Enti collegati al convegno:||Italian Society of Neural Networks|
|Digital Object Identifier (DOI):||http://dx.doi.org/10.3233/978-1-60750-972-1-129|
|Tipologia:||Book Part (author)|
|Appare nelle tipologie:||03 - Contributo in volume|