Abstract. In this paper we formulate the task of semantic image segmentation as a manifold embedding problem and solve it using graph Laplacian approximation. This allows for unsupervised learning of graph Laplacian parameters individually for each image without using any prior information. We perform experiments on GrabCut, Graz and Pascal datasets. At a low computational cost proposed learning method shows comparable performance to choosing the parameters on the test set. Our framework for semantic image segmentation shows better performance than the standard discrete CRF with graph-cut inference.
The reduction in the number of deposits reaching the modern erosional surface has led to the need to develop and improve the geochemical exploration methods for searching for deposits under deep cover. They are focused on the identification of gas and lithochemical halos of indirect (CO2, H2, He, NH4+, K+ ions, etc.) and direct indicators (Au, Pb, Cu, etc.) for buried ore bodies and blind deposits. The data of experimental and methodical works for numerous deposits of different genesis suggest that the formation of superimposed halos of these two groups of indicators of ore-forming environment proceeds by a single mechanism. Their joint migration to the day surface and the subsequent unloading into the atmosphere form halos in the subsurface air, where the concentration of the indicative elements many times exceeds the geochemical background. That allows laser analytical equipment used in meteorology (lidars, correlation spectrometers, etc.) to be applied during fi ldwork to monitor the composition of the atmosphere.
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