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• W2794870879 abstract "To parse large-scale urban scenes using the supervised methods, a large amount of training data that can account for the vast visual and structural variance of urban environment is necessary. Unfortunately, such training data are mostly obtained by tedious and time-consuming manual work. To overcome the drawback, we propose a semisupervised learning framework that combines the margin, cograph, and label constraints into an objective function for point cloud parsing. Mathematically, the margin constraint is presented to learn a novel distance criterion that can effectively recognize points of different classes. The graph regularization is then employed to characterize the intrinsic geometry structure of the data manifold and explore relationships among points. The label consistency regularization is introduced to ensure the category consistency of the clustered points and single point. To classify the out-of-sample data, the framework successfully transforms the semisupervised classification results into the linear classifier by adopting a linear regression. An iterative algorithm is utilized to efficiently and effectively optimize the objective function with characteristics of multiple variables and highly nonlinear. The point clouds of four urban scenes are used to validate our method. The experimental results show that our method outperforms the state-of-the-art algorithms." @default.
• W2794870879 created "2018-04-06" @default.
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• W2794870879 date "2018-07-01" @default.
• W2794870879 modified "2023-10-18" @default.
• W2794870879 title "Joint Margin, Cograph, and Label Constraints for Semisupervised Scene Parsing From Point Clouds" @default.
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• W2794870879 doi "https://doi.org/10.1109/tgrs.2018.2811748" @default.
• W2794870879 hasPublicationYear "2018" @default.
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