Haoran Dai | Hai Lin

Graph convolutional network-based semi-supervisedfeature classiﬁcation of volumes

Thu, 15 Sep 2022 16:01:45 +0800

Feature classiﬁcation has always been one of the research hotspots in scientiﬁc visualization.However, conventional interactive feature classiﬁcation methods rely on prior knowledge and typicallyrequire trial and error, whereas feature classiﬁcation based on data mining is generally based on localfeatures; therefore, obtaining good results with traditional methods is difﬁcult. In this paper, we ﬁrst map avolume to the super-voxel graph using a 3D extension of the simple linear iterative clustering algorithm andthen construct a graph convolutional neural network to implement node classiﬁcation in a semi-supervisedway, i.e., a small number of user-labeled super-voxels. We transform the feature classiﬁcation of a volumeinto the classiﬁcation task of nodes of a super-voxel graph, which is a novel approach and broadens theapplication scope of graph neural netwo rk to volumes. Experiments on different volumes have demonstratedthe strong learning ability and reasoning ability of the proposed method.

Voxel2vec

Tue, 12 Jul 2022 16:33:15 +0800

Relationships in scientific data are intricate and complex, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations’ relations in multivariate data, and the association of volumes in time-varying and ensemble data. This paper presents voxel2vec, a novel unsupervised representation learning model, to learn distributed representations of scalar values in a low-dimensional vector space. The basic assumption is that if two scalar values/scalar-value combinations have similar contexts, they usually have high similarity in terms of features. By representing scalar values as symbols, voxel2vec learns the similarity of scalar values in the context of spatial distribution and then we can explore the overall association between volumes by transfer prediction. We demonstrate the usefulness and effectiveness of voxel2vec by comparing it with the isosurface similarity map of univariate data and applying the learned distributed representations to feature classification for multivariate data and association analysis for time-varying and ensemble data.

Graph convolutional network-based semi-supervised feature classification of volumes

Sat, 01 Jan 2022 00:00:00 +0000

voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data

Sat, 01 Jan 2022 00:00:00 +0000

IsoExplorer: an isosurface-driven framework for 3D shape analysis of biomedical volume data

Fri, 01 Jan 2021 00:00:00 +0000

Visual analytics of urban transportation from a bike-sharing and taxi perspective

Wed, 01 Jan 2020 00:00:00 +0000