Clustering by Scale-Space Filtering

By Yee Leung, Jiangshe Zhang, Zongben Xu. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000

316 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/50017bbc24d485721f71c51c3e1d4e9d06d81575

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In Computer Vision and Pattern Recognition

In Media Technology

Citations in the corpus

A Review on Modal Clustering (2016, methodology): “Within the set of clustering methods performing mode seeking, we also recall the R package Modalclust (Cheng & Ray, 2014b), which performs the method of Li et al. (2007) and its parallelised implementation, which timely speeds up computations.”
An Algorithm for Data-Driven Bandwidth Selection (2003, methodology): “This strategy is also implemented within the framework of scale-space theory [17].”
Dynamic clustering using particle swarm optimization with application in image segmentation (2006, methodology): “In hierarchical clustering, the output is ‘‘a tree showing a sequence of clustering with each clustering being a partition of the data set’’ [ 12 ].”
Determination of the spread parameter in the Gaussian kernel for classification and regression (2003, background): “For a given data set, in scale space, each data point can be regarded as a light dot, whose distribution can be represented by Dirac function $(x − xi) [10].”
Survey of clustering algorithms (2005, background): “We offer more detailed discussion on how to identify appropriate number of clusters, which is particularly important in cluster validity, in the last part of the section.”
A survey on nature inspired metaheuristic algorithms for partitional clustering (2014, background): “The hierarchical algorithms provide a tree structure output (dendrogram plot) which represent the nested grouping of the elements of a dataset [6,7].”
Interpretation and Generalization of Score Matching (2009, methodology): “Indeed, this methodology has been adopted in clustering and non-parametric density estimation, e.g., [ LZX00 ].”
Quantitative analysis of cryo-EM density map segmentation by watershed and scale-space filtering, and fitting of structures by alignment to regions (2010, background): “gions while retaining salient features (Witkin, 1984; Perona and Malik, 1990; Lifshitz et al., 1990; Lindeberg, 1996; Ren, 2008; Braga-Neto and Goutsias, 2005; Leung et al., 2000).”