A fast directed tree based neighborhood clustering algorithm for image segmentation

Authors:
Jundi Ding;SongCan Chen;RuNing Ma;Bo Wang
Affiliations:
College of Information Science & Technology, Nanjing University of Aeronautics and Astronautics, P.R. China;College of Information Science & Technology, Nanjing University of Aeronautics and Astronautics, P.R. China;College of Science, Nanjing University of Aeronautics and Astronautics, P.R. China;College of Information Science & Technology, Nanjing University of Aeronautics and Astronautics, P.R. China
Venue:
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Year:
2006

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Abstract

First, a modified Neighborhood-Based Clustering (MNBC) algorithm using the directed tree for data clustering is presented. It represents a dataset as some directed trees corresponding to meaningful clusters. Governed by Neighborhood-based Density Factor (NDF), it also can discover clusters of arbitrary shape and different densities like NBC. Moreover, it greatly simplify NBC. However, a failure applying in image segmentation is due to an unsuitable use of Euclidean distance between image pixels. Second, Gray NDF (GNDF) is introduced to make MNBC suitable for image segmentation. The dataset to be segmented is all grays and thus MNBC has the constant computational complexity O(256). The experiments on synthetic datasets and real-world images shows that MNBC outperforms some existing graph-theoretical approaches in terms of computation time as well as segmentation effect.