RuNing Ma

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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)
In this paper, we propose a normalized path-based metric based on an introduced neighborhood density index which can sufficiently exploit the local density " revealed " by data. The metric axioms (positive definite property, symmetry and triangular inequality) are strictly proved in theory. Using this idea of path, we further devise a heuristic clustering(More)
—Interactive object segmentation is an active research area in recent decades. The common practice is to leave interactions to be set manually by users in advance. Often times, to get good interactions, one has to struggle with laborious local editing for re-correcting. Given the larger and larger databases occurred nowadays, it is impractical for one to(More)
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