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The topology (structure) of the building is stored in the form of a labelled and attributed directed graph.
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In graph-theoretic notions we also talk about labelled and directed attributed multigraphs in this case.
Obviously, the schema is again a labelled and directed attributed multigraph.
We use attributed, labelled and edge-directed graphs.
Every day works labeled "original" and "authentic" and attributed to titans of the art world are offered at closeout prices by online galleries and auction sites.
In this visualization labels and attributes are omitted, only the topological structure of the building is shown.
The RBF kernel nonlinearly maps samples into a higher dimensional space to handle cases where the relation between class labels and attributes is nonlinear.
This RBF kernel nonlinearly maps samples into a higher dimensional space so it, unlike the linear kernel, can handle the case when the relation between class labels and attributes is nonlinear.
In our work, we used a radial basis function (RBF) as the kernel function, given that it can handle the case when the relation between class labels and attributes is nonlinear [ 31].
In typical use scenarios, VANO is started (i) on just a raw image with no objects; (ii) with a segmentation mask produced by a computation (e.g. Long et al., 2007); or (iii) with both a segmentation mask and an initial annotation (i.e. label and attributes), produced again by an automated computation (e.g. Long et al., 2008a) or other means.
For instance, Carson et al. [25] present a blob-based segmentation method that models the color, texture, and position of all the pixels in a given image with a Gaussian mixture model (GMM), and attribute the label of its most likely GMM component to each pixel.
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