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- W1549232004 abstract "Most cost function based clustering or partitioning methods measure the compactness of groups of data. In contrast to this picture of a point source in feature space, some data sources are spread out on a low-dimensional manifold which is embedded in a high dimensional data space. This property is adequately captured by the criterion of connectedness which is approximated by graph theoretic partitioning methods. We propose in this paper a pairwise clustering cost function with a novel dissimilarity measure emphasizing connectedness in feature space rather than compactness. The connectedness criterion considers two objects as similar if there exists a mediating intra cluster path without an edge with large cost. The cost function is optimized in a multi-scale fashion. This new path based clustering concept is applied to segment textured images with strong texture gradients based on dissimilarities between image patches." @default.
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- W1549232004 date "2001-01-01" @default.
- W1549232004 modified "2023-10-01" @default.
- W1549232004 title "Path Based Pairwise Data Clustering with Application to Texture Segmentation" @default.
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- W1549232004 doi "https://doi.org/10.1007/3-540-44745-8_16" @default.
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