Small-variance nonparametric clustering on the hypersphere
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How_Small-variance.pdf
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11.23 MB
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Adobe PDF
Checksum (MD5)
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Author(s)
Straub, Julian
Campbell, Trevor David
How, Jonathan P
Fisher, John W
Date Issued
October 2015
Journal
IEEE Conference on Computer Vision and Pattern Recognition, 2015. CVPR 2015.
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Straub, Julian et al. “Small-Variance Nonparametric Clustering on the Hypersphere.” IEEE, 2015. 334–342.
Version
Author's final manuscript
Abstract
Structural regularities in man-made environments reflect in the distribution of their surface normals. Describing these surface normal distributions is important in many computer vision applications, such as scene understanding, plane segmentation, and regularization of 3D reconstructions. Based on the small-variance limit of Bayesian nonparametric von-Mises-Fisher (vMF) mixture distributions, we propose two new flexible and efficient k-means-like clustering algorithms for directional data such as surface normals. The first, DP-vMF-means, is a batch clustering algorithm derived from the Dirichlet process (DP) vMF mixture. Recognizing the sequential nature of data collection in many applications, we extend this algorithm to DDP-vMF-means, which infers temporally evolving cluster structure from streaming data. Both algorithms naturally respect the geometry of directional data, which lies on the unit sphere. We demonstrate their performance on synthetic directional data and real 3D surface normals from RGB-D sensors. While our experiments focus on 3D data, both algorithms generalize to high dimensional directional data such as protein backbone configurations and semantic word vectors.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
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Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR.2015.7298630
