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.
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Ding, J., Chen, S., Ma, R., Wang, B. (2006). A Fast Directed Tree Based Neighborhood Clustering Algorithm for Image Segmentation. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_41
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DOI: https://doi.org/10.1007/11893257_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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