Journal
JOURNAL OF MATHEMATICAL IMAGING AND VISION
Volume 58, Issue 3, Pages 468-493Publisher
SPRINGER
DOI: 10.1007/s10851-017-0713-9
Keywords
Variational methods; Graphical models; Convex optimization; Semi-supervised classification; Point cloud segmentation
Ask authors/readers for more resources
Graph-based variational methods have recently shown to be highly competitive for various classification problems of high-dimensional data, but are inherently difficult to handle from an optimization perspective. This paper proposes a convex relaxation for a certain set of graph-based multiclass data segmentation models involving a graph total variation term, region homogeneity terms, supervised information and certain constraints or penalty terms acting on the class sizes. Particular applications include semi-supervised classification of high-dimensional data and unsupervised segmentation of unstructured 3D point clouds. Theoretical analysis shows that the convex relaxation closely approximates the original NP-hard problems, and these observations are also confirmed experimentally. An efficient duality-based algorithm is developed that handles all constraints on the labeling function implicitly. Experiments on semi-supervised classification indicate consistently higher accuracies than related non-convex approaches and considerably so when the training data are not uniformly distributed among the data set. The accuracies are also highly competitive against a wide range of other established methods on three benchmark data sets. Experiments on 3D point clouds acquire by a LaDAR in outdoor scenes and demonstrate that the scenes can accurately be segmented into object classes such as vegetation, the ground plane and human-made structures.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available