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This paper presents a connected coherence tree algorithm (CCTA) for image segmentation with no prior knowledge. It aims to find regions of semantic coherence based on the proposed epsiv-neighbor coherence segmentation criterion. More specifically, with an adaptive spatial scale and an appropriate intensity-difference scale, CCTA often achieves several sets(More)
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(More)
How can we find a natural clustering of a ‘‘complex’’ dataset, which may contain an unknown number of overlapping clusters of arbitrary shape and be contaminated by noise? A tree-structured framework is proposed in this paper to purify such clusters by exploring the structural role of each data. In practice, each individual object within the internal(More)
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